Python tensorflow.python.platform.tf_logging.set_verbosity() Examples

The following are 30 code examples of tensorflow.python.platform.tf_logging.set_verbosity(). 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 tensorflow.python.platform.tf_logging , or try the search function .
Example #1
Source File: conll2tree.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #2
Source File: conll2tree.py    From hands-detection with MIT License 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #3
Source File: conll2tree.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #4
Source File: conll2tree.py    From ECO-pytorch with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #5
Source File: conll2tree.py    From object_detection_with_tensorflow with MIT License 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #6
Source File: conll2tree.py    From Action_Recognition_Zoo with MIT License 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #7
Source File: conll2tree.py    From AI_Reader with Apache License 2.0 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        print tr(d)

      if finished:
        break
Example #8
Source File: conll2tree.py    From HumanRecognition with MIT License 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #9
Source File: conll2tree.py    From Gun-Detector with Apache License 2.0 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print('Input: %s' % sentence.text)
        print('Parse:')
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print(pat.sub('', tr_ln))

      if finished:
        break
Example #10
Source File: conll2tree.py    From g-tensorflow-models with Apache License 2.0 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #11
Source File: conll2tree.py    From yolo_v2 with Apache License 2.0 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #12
Source File: conll2tree.py    From multilabel-image-classification-tensorflow with MIT License 6 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    src = gen_parser_ops.document_source(batch_size=32,
                                         corpus_name=FLAGS.corpus_name,
                                         task_context=FLAGS.task_context)
    sentence = sentence_pb2.Sentence()
    while True:
      documents, finished = sess.run(src)
      logging.info('Read %d documents', len(documents))
      for d in documents:
        sentence.ParseFromString(d)
        tr = asciitree.LeftAligned()
        d = to_dict(sentence)
        print 'Input: %s' % sentence.text
        print 'Parse:'
        tr_str = tr(d)
        pat = re.compile(r'\s*@\d+$')
        for tr_ln in tr_str.splitlines():
          print pat.sub('', tr_ln)

      if finished:
        break
Example #13
Source File: dragnn_parser.py    From syntaxnet-rest-api with GNU General Public License v3.0 5 votes vote down vote up 
def load_model(self,base_dir, master_spec_name, checkpoint_name):
        # Read the master spec
        master_spec = spec_pb2.MasterSpec()
        with open(os.path.join(base_dir, master_spec_name), "r") as f:
            text_format.Merge(f.read(), master_spec)
        spec_builder.complete_master_spec(master_spec, None, base_dir)
        logging.set_verbosity(logging.WARN)  # Turn off TensorFlow spam.

        # Initialize a graph
        graph = tf.Graph()
        with graph.as_default():
            hyperparam_config = spec_pb2.GridPoint()
            builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)
            # This is the component that will annotate test sentences.
            annotator = builder.add_annotation(enable_tracing=True)
            builder.add_saver()  # "Savers" can save and load models; here, we're only going to load.

        sess = tf.Session(graph=graph)
        with graph.as_default():
            # sess.run(tf.global_variables_initializer())
            # sess.run('save/restore_all', {'save/Const:0': os.path.join(base_dir, checkpoint_name)})
            builder.saver.restore(sess, os.path.join(base_dir, checkpoint_name))

        def annotate_sentence(sentence):
            with graph.as_default():
                return sess.run([annotator['annotations'], annotator['traces']],
                                feed_dict={annotator['input_batch']: [sentence]})

        return annotate_sentence
Example #14
Source File: parser_eval.py    From AI_Reader with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    feature_sizes, domain_sizes, embedding_dims, num_actions = sess.run(
        gen_parser_ops.feature_size(task_context=FLAGS.task_context,
                                    arg_prefix=FLAGS.arg_prefix))

  with tf.Session() as sess:
    Eval(sess, num_actions, feature_sizes, domain_sizes, embedding_dims)
Example #15
Source File: parser_eval.py    From HumanRecognition with MIT License 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #16
Source File: parser_eval.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #17
Source File: parser_eval.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #18
Source File: parser_eval.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #19
Source File: parser_eval.py    From multilabel-image-classification-tensorflow with MIT License 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #20
Source File: parser_eval.py    From hands-detection with MIT License 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #21
Source File: parser_eval.py    From DOTA_models with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #22
Source File: parser_eval.py    From ECO-pytorch with BSD 2-Clause "Simplified" License 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #23
Source File: parser_eval.py    From Action_Recognition_Zoo with MIT License 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #24
Source File: parser_eval.py    From yolo_v2 with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #25
Source File: ParseyPredFace.py    From PredPatt with BSD 3-Clause "New" or "Revised" License 5 votes vote down vote up 
def load_model(base_dir, master_spec_name, checkpoint_name):
    """
    Function to load the syntaxnet models. Highly specific to the tutorial
    format right now.
    """
    # Read the master spec
    master_spec = spec_pb2.MasterSpec()
    with open(os.path.join(base_dir, master_spec_name), "r") as f:
        text_format.Merge(f.read(), master_spec)
    spec_builder.complete_master_spec(master_spec, None, base_dir)
    logging.set_verbosity(logging.WARN)  # Turn off TensorFlow spam.

    # Initialize a graph
    graph = tf.Graph()
    with graph.as_default():
        hyperparam_config = spec_pb2.GridPoint()
        builder = graph_builder.MasterBuilder(master_spec, hyperparam_config)
        # This is the component that will annotate test sentences.
        annotator = builder.add_annotation(enable_tracing=True)
        builder.add_saver()  # "Savers" can save and load models; here, we're only going to load.

    sess = tf.Session(graph=graph)
    with graph.as_default():
        #sess.run(tf.global_variables_initializer())
        #sess.run('save/restore_all', {'save/Const:0': os.path.join(base_dir, checkpoint_name)})
        builder.saver.restore(sess, os.path.join(base_dir, checkpoint_name))

    def annotate_sentence(sentence):
        with graph.as_default():
            return sess.run([annotator['annotations'], annotator['traces']],
                            feed_dict={annotator['input_batch']: [sentence]})
    return annotate_sentence
Example #26
Source File: parser_eval.py    From Gun-Detector with Apache License 2.0 5 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  with tf.Session() as sess:
    Eval(sess)
Example #27
Source File: parser_trainer.py    From DOTA_models with Apache License 2.0 4 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  if not gfile.IsDirectory(OutputPath('')):
    gfile.MakeDirs(OutputPath(''))

  # Rewrite context.
  RewriteContext()

  # Creates necessary term maps.
  if FLAGS.compute_lexicon:
    logging.info('Computing lexicon...')
    with tf.Session(FLAGS.tf_master) as sess:
      gen_parser_ops.lexicon_builder(task_context=OutputPath('context'),
                                     corpus_name=FLAGS.training_corpus).run()
  with tf.Session(FLAGS.tf_master) as sess:
    feature_sizes, domain_sizes, embedding_dims, num_actions = sess.run(
        gen_parser_ops.feature_size(task_context=OutputPath('context'),
                                    arg_prefix=FLAGS.arg_prefix))

  # Well formed and projectivize.
  if FLAGS.projectivize_training_set:
    logging.info('Preprocessing...')
    with tf.Session(FLAGS.tf_master) as sess:
      source, last = gen_parser_ops.document_source(
          task_context=OutputPath('context'),
          batch_size=FLAGS.batch_size,
          corpus_name=FLAGS.training_corpus)
      sink = gen_parser_ops.document_sink(
          task_context=OutputPath('context'),
          corpus_name='projectivized-training-corpus',
          documents=gen_parser_ops.projectivize_filter(
              gen_parser_ops.well_formed_filter(source,
                                                task_context=OutputPath(
                                                    'context')),
              task_context=OutputPath('context')))
      while True:
        tf_last, _ = sess.run([last, sink])
        if tf_last:
          break

  logging.info('Training...')
  with tf.Session(FLAGS.tf_master) as sess:
    Train(sess, num_actions, feature_sizes, domain_sizes, embedding_dims)
Example #28
Source File: parser_trainer.py    From multilabel-image-classification-tensorflow with MIT License 4 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  if not gfile.IsDirectory(OutputPath('')):
    gfile.MakeDirs(OutputPath(''))

  # Rewrite context.
  RewriteContext()

  # Creates necessary term maps.
  if FLAGS.compute_lexicon:
    logging.info('Computing lexicon...')
    with tf.Session(FLAGS.tf_master) as sess:
      gen_parser_ops.lexicon_builder(task_context=OutputPath('context'),
                                     corpus_name=FLAGS.training_corpus).run()
  with tf.Session(FLAGS.tf_master) as sess:
    feature_sizes, domain_sizes, embedding_dims, num_actions = sess.run(
        gen_parser_ops.feature_size(task_context=OutputPath('context'),
                                    arg_prefix=FLAGS.arg_prefix))

  # Well formed and projectivize.
  if FLAGS.projectivize_training_set:
    logging.info('Preprocessing...')
    with tf.Session(FLAGS.tf_master) as sess:
      source, last = gen_parser_ops.document_source(
          task_context=OutputPath('context'),
          batch_size=FLAGS.batch_size,
          corpus_name=FLAGS.training_corpus)
      sink = gen_parser_ops.document_sink(
          task_context=OutputPath('context'),
          corpus_name='projectivized-training-corpus',
          documents=gen_parser_ops.projectivize_filter(
              gen_parser_ops.well_formed_filter(source,
                                                task_context=OutputPath(
                                                    'context')),
              task_context=OutputPath('context')))
      while True:
        tf_last, _ = sess.run([last, sink])
        if tf_last:
          break

  logging.info('Training...')
  with tf.Session(FLAGS.tf_master) as sess:
    Train(sess, num_actions, feature_sizes, domain_sizes, embedding_dims)
Example #29
Source File: parser_trainer.py    From yolo_v2 with Apache License 2.0 4 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  if not gfile.IsDirectory(OutputPath('')):
    gfile.MakeDirs(OutputPath(''))

  # Rewrite context.
  RewriteContext()

  # Creates necessary term maps.
  if FLAGS.compute_lexicon:
    logging.info('Computing lexicon...')
    with tf.Session(FLAGS.tf_master) as sess:
      gen_parser_ops.lexicon_builder(task_context=OutputPath('context'),
                                     corpus_name=FLAGS.training_corpus).run()
  with tf.Session(FLAGS.tf_master) as sess:
    feature_sizes, domain_sizes, embedding_dims, num_actions = sess.run(
        gen_parser_ops.feature_size(task_context=OutputPath('context'),
                                    arg_prefix=FLAGS.arg_prefix))

  # Well formed and projectivize.
  if FLAGS.projectivize_training_set:
    logging.info('Preprocessing...')
    with tf.Session(FLAGS.tf_master) as sess:
      source, last = gen_parser_ops.document_source(
          task_context=OutputPath('context'),
          batch_size=FLAGS.batch_size,
          corpus_name=FLAGS.training_corpus)
      sink = gen_parser_ops.document_sink(
          task_context=OutputPath('context'),
          corpus_name='projectivized-training-corpus',
          documents=gen_parser_ops.projectivize_filter(
              gen_parser_ops.well_formed_filter(source,
                                                task_context=OutputPath(
                                                    'context')),
              task_context=OutputPath('context')))
      while True:
        tf_last, _ = sess.run([last, sink])
        if tf_last:
          break

  logging.info('Training...')
  with tf.Session(FLAGS.tf_master) as sess:
    Train(sess, num_actions, feature_sizes, domain_sizes, embedding_dims)
Example #30
Source File: parser_trainer.py    From g-tensorflow-models with Apache License 2.0 4 votes vote down vote up 
def main(unused_argv):
  logging.set_verbosity(logging.INFO)
  if not gfile.IsDirectory(OutputPath('')):
    gfile.MakeDirs(OutputPath(''))

  # Rewrite context.
  RewriteContext()

  # Creates necessary term maps.
  if FLAGS.compute_lexicon:
    logging.info('Computing lexicon...')
    with tf.Session(FLAGS.tf_master) as sess:
      gen_parser_ops.lexicon_builder(task_context=OutputPath('context'),
                                     corpus_name=FLAGS.training_corpus).run()
  with tf.Session(FLAGS.tf_master) as sess:
    feature_sizes, domain_sizes, embedding_dims, num_actions = sess.run(
        gen_parser_ops.feature_size(task_context=OutputPath('context'),
                                    arg_prefix=FLAGS.arg_prefix))

  # Well formed and projectivize.
  if FLAGS.projectivize_training_set:
    logging.info('Preprocessing...')
    with tf.Session(FLAGS.tf_master) as sess:
      source, last = gen_parser_ops.document_source(
          task_context=OutputPath('context'),
          batch_size=FLAGS.batch_size,
          corpus_name=FLAGS.training_corpus)
      sink = gen_parser_ops.document_sink(
          task_context=OutputPath('context'),
          corpus_name='projectivized-training-corpus',
          documents=gen_parser_ops.projectivize_filter(
              gen_parser_ops.well_formed_filter(source,
                                                task_context=OutputPath(
                                                    'context')),
              task_context=OutputPath('context')))
      while True:
        tf_last, _ = sess.run([last, sink])
        if tf_last:
          break

  logging.info('Training...')
  with tf.Session(FLAGS.tf_master) as sess:
    Train(sess, num_actions, feature_sizes, domain_sizes, embedding_dims)