Python skipthoughts.eval_msrp.evaluate() Examples

The following are 11 code examples of skipthoughts.eval_msrp.evaluate(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module skipthoughts.eval_msrp , or try the search function .
Example #1
Source File: evaluate.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()
Example #2
Source File: evaluate.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()
Example #3
Source File: evaluate.py    From parallax with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
    if not FLAGS.data_dir:
        raise ValueError("--data_dir is required.")

    encoder = encoder_manager.EncoderManager()

    # Maybe load unidirectional encoder.
    if FLAGS.uni_checkpoint_path:
        print("Loading unidirectional model...")
        uni_config = configuration.model_config()
        encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                           FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

    # Maybe load bidirectional encoder.
    if FLAGS.bi_checkpoint_path:
        print("Loading bidirectional model...")
        bi_config = configuration.model_config(bidirectional_encoder=True)
        encoder.load_model(bi_config, FLAGS.bi_vocab_file,
                           FLAGS.bi_embeddings_file,
                           FLAGS.bi_checkpoint_path)

    if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
        eval_classification.eval_nested_kfold(
            encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
    elif FLAGS.eval_task == "SICK":
        eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
    elif FLAGS.eval_task == "MSRP":
        eval_msrp.evaluate(
            encoder, evalcv=True, evaltest=True, use_feats=True,
            loc=FLAGS.data_dir)
    elif FLAGS.eval_task == "TREC":
        eval_trec.evaluate(encoder, evalcv=True, evaltest=True,
                           loc=FLAGS.data_dir)
    else:
        raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

    encoder.close()
Example #4
Source File: evaluate.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()
Example #5
Source File: evaluate.py    From hands-detection with MIT License 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()
Example #6
Source File: evaluate.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()
Example #7
Source File: evaluate.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()
Example #8
Source File: evaluate.py    From HumanRecognition with MIT License 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()
Example #9
Source File: evaluate.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()
Example #10
Source File: evaluate.py    From models with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()
Example #11
Source File: evaluate.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def main(unused_argv):
  if not FLAGS.data_dir:
    raise ValueError("--data_dir is required.")

  encoder = encoder_manager.EncoderManager()

  # Maybe load unidirectional encoder.
  if FLAGS.uni_checkpoint_path:
    print("Loading unidirectional model...")
    uni_config = configuration.model_config()
    encoder.load_model(uni_config, FLAGS.uni_vocab_file,
                       FLAGS.uni_embeddings_file, FLAGS.uni_checkpoint_path)

  # Maybe load bidirectional encoder.
  if FLAGS.bi_checkpoint_path:
    print("Loading bidirectional model...")
    bi_config = configuration.model_config(bidirectional_encoder=True)
    encoder.load_model(bi_config, FLAGS.bi_vocab_file, FLAGS.bi_embeddings_file,
                       FLAGS.bi_checkpoint_path)

  if FLAGS.eval_task in ["MR", "CR", "SUBJ", "MPQA"]:
    eval_classification.eval_nested_kfold(
        encoder, FLAGS.eval_task, FLAGS.data_dir, use_nb=False)
  elif FLAGS.eval_task == "SICK":
    eval_sick.evaluate(encoder, evaltest=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "MSRP":
    eval_msrp.evaluate(
        encoder, evalcv=True, evaltest=True, use_feats=True, loc=FLAGS.data_dir)
  elif FLAGS.eval_task == "TREC":
    eval_trec.evaluate(encoder, evalcv=True, evaltest=True, loc=FLAGS.data_dir)
  else:
    raise ValueError("Unrecognized eval_task: %s" % FLAGS.eval_task)

  encoder.close()