Python v1.parse_single_example方法代碼示例

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def _parse_fn(self, value):
    """Parses an image and its label from a serialized TFExample.

    Args:
      value: serialized string containing an TFExample.

    Returns:
      Returns a tuple of (image, label) from the TFExample.
    """
    if FLAGS.get_flag_value('pseudo_label_key', None):
      self.ORIGINAL_LABEL_KEY = FLAGS.get_flag_value(
          'original_label_key', None)
      assert self.ORIGINAL_LABEL_KEY is not None, (
          'You must set original_label_key for pseudo labeling.')

      #Replace original label_key with pseudo_label_key.
      self.LABEL_KEY = FLAGS.get_flag_value('pseudo_label_key', None)
      self.FEATURE_MAP.update({
          self.LABEL_KEY: tf.FixedLenFeature(shape=[], dtype=tf.int64),
          self.ORIGINAL_LABEL_KEY: tf.FixedLenFeature(
              shape=[], dtype=tf.int64),
          self.FLAG_KEY: tf.FixedLenFeature(shape=[], dtype=tf.int64),
      })
    return tf.parse_single_example(value, self.FEATURE_MAP) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def __call__(self, example_string):
    """Processes a single example string.

    Extracts and processes the feature, and ignores the label.

    Args:
      example_string: str, an Example protocol buffer.

    Returns:
      feat: The feature tensor.
    """
    feat = tf.parse_single_example(
        example_string,
        features={
            'image/embedding':
                tf.FixedLenFeature([self.feat_len], dtype=tf.float32),
            'image/class/label':
                tf.FixedLenFeature([], tf.int64)
        })['image/embedding']

    return feat 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def tfrecord_iterator(filenames, gzipped=False, example_spec=None):
  """Yields records from TFRecord files.

  Args:
    filenames: list<str>, list of TFRecord filenames to read from.
    gzipped: bool, whether the TFRecord files are gzip-encoded.
    example_spec: dict<str feature name, tf.VarLenFeature/tf.FixedLenFeature>,
      if provided, will parse each record as a tensorflow.Example proto.

  Yields:
    Records (or parsed Examples, if example_spec is provided) from files.
  """
  with tf.Graph().as_default():
    dataset = tf.data.Dataset.from_tensor_slices(filenames)

    def _load_records(filename):
      return tf.data.TFRecordDataset(
          filename,
          compression_type=tf.constant("GZIP") if gzipped else None,
          buffer_size=16 * 1000 * 1000)

    dataset = dataset.flat_map(_load_records)

    def _parse_example(ex_ser):
      return tf.parse_single_example(ex_ser, example_spec)

    if example_spec:
      dataset = dataset.map(_parse_example, num_parallel_calls=32)
    dataset = dataset.prefetch(100)
    record_it = dataset.make_one_shot_iterator().get_next()

    with tf.Session() as sess:
      while True:
        try:
          ex = sess.run(record_it)
          yield ex
        except tf.errors.OutOfRangeError:
          break 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def _decode_record(record, name_to_features):
    """Decodes a record to a TensorFlow example."""
    example = tf.parse_single_example(record, name_to_features)

    # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
    # So cast all int64 to int32.
    for name in list(example.keys()):
        t = example[name]
        if t.dtype == tf.int64:
            t = tf.cast(t, tf.int32)
        example[name] = t
    return example 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def _decode_record(record, name_to_features):
  """Decodes a record to a TensorFlow example."""
  example = tf.parse_single_example(record, name_to_features)

  # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
  # So cast all int64 to int32.
  for name in list(example.keys()):
    t = example[name]
    if t.dtype == tf.int64:
      t = tf.to_int32(t)
    example[name] = t

  return example 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def parse_example(example_proto):
  features = {
      'id': tf.FixedLenFeature(shape=(), dtype=tf.string),
      'sequence': tf.FixedLenFeature(shape=(), dtype=tf.string),
      'audio': tf.FixedLenFeature(shape=(), dtype=tf.string),
      'velocity_range': tf.FixedLenFeature(shape=(), dtype=tf.string),
  }
  record = tf.parse_single_example(example_proto, features)
  return record 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def parse_preprocessed_example(example_proto):
  """Process an already preprocessed Example proto into input tensors."""
  features = {
      'spec': tf.VarLenFeature(dtype=tf.float32),
      'spectrogram_hash': tf.FixedLenFeature(shape=(), dtype=tf.int64),
      'labels': tf.VarLenFeature(dtype=tf.float32),
      'label_weights': tf.VarLenFeature(dtype=tf.float32),
      'length': tf.FixedLenFeature(shape=(), dtype=tf.int64),
      'onsets': tf.VarLenFeature(dtype=tf.float32),
      'offsets': tf.VarLenFeature(dtype=tf.float32),
      'velocities': tf.VarLenFeature(dtype=tf.float32),
      'sequence_id': tf.FixedLenFeature(shape=(), dtype=tf.string),
      'note_sequence': tf.FixedLenFeature(shape=(), dtype=tf.string),
  }
  record = tf.parse_single_example(example_proto, features)
  input_tensors = InputTensors(
      spec=tf.sparse.to_dense(record['spec']),
      spectrogram_hash=record['spectrogram_hash'],
      labels=tf.sparse.to_dense(record['labels']),
      label_weights=tf.sparse.to_dense(record['label_weights']),
      length=record['length'],
      onsets=tf.sparse.to_dense(record['onsets']),
      offsets=tf.sparse.to_dense(record['offsets']),
      velocities=tf.sparse.to_dense(record['velocities']),
      sequence_id=record['sequence_id'],
      note_sequence=record['note_sequence'])
  return input_tensors 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def get_example(self, batch_size):
    """Get a single example from the tfrecord file.

    Args:
      batch_size: Int, minibatch size.

    Returns:
      tf.Example protobuf parsed from tfrecord.
    """
    reader = tf.TFRecordReader()
    num_epochs = None if self.is_training else 1
    capacity = batch_size
    path_queue = tf.train.input_producer(
        [self.record_path],
        num_epochs=num_epochs,
        shuffle=self.is_training,
        capacity=capacity)
    unused_key, serialized_example = reader.read(path_queue)
    features = {
        "note_str": tf.FixedLenFeature([], dtype=tf.string),
        "pitch": tf.FixedLenFeature([1], dtype=tf.int64),
        "velocity": tf.FixedLenFeature([1], dtype=tf.int64),
        "audio": tf.FixedLenFeature([64000], dtype=tf.float32),
        "qualities": tf.FixedLenFeature([10], dtype=tf.int64),
        "instrument_source": tf.FixedLenFeature([1], dtype=tf.int64),
        "instrument_family": tf.FixedLenFeature([1], dtype=tf.int64),
    }
    example = tf.parse_single_example(serialized_example, features)
    return example 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def parse_example(serialized_example):
  """Parse example."""
  features = tf.parse_single_example(
      serialized_example,
      features={
          "question": tf.FixedLenFeature([], tf.string),
          "context": tf.FixedLenFeature([], tf.string),
          "answer_start": tf.FixedLenFeature([], tf.int64),
          "answer_end": tf.FixedLenFeature([], tf.int64),
      })
  features["question"] = split_on_whitespace(features["question"])
  features["context"] = split_on_whitespace(features["context"])
  features["answer_start"] = tf.to_int32(features["answer_start"])
  features["answer_end"] = tf.to_int32(features["answer_end"])
  return features 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def _get_cached_dataset(self, split=tfds.Split.TRAIN, shuffle=True):
    """Returns a tf.data.Dataset read from cached files."""
    self.assert_cached()
    with tf.io.gfile.GFile(get_info_path(self.cache_dir, split)) as f:
      split_info = json.load(f)

    # Use `FixedLenSequenceFeature` for sequences with variable length.
    def _feature_config(shape, dtype):
      if shape and shape[0] is None:
        return tf.io.FixedLenSequenceFeature(
            shape[1:], dtype, allow_missing=True)
      return tf.io.FixedLenFeature(shape, dtype)
    feature_desc = {
        feat: _feature_config(**desc)
        for feat, desc in split_info["features"].items()}

    ds = tf.data.Dataset.list_files(
        "%s-*-of-*%d" % (
            get_tfrecord_prefix(self.cache_dir, split),
            split_info["num_shards"]),
        shuffle=shuffle)
    ds = ds.interleave(
        tf.data.TFRecordDataset,
        cycle_length=16, block_length=16,
        num_parallel_calls=tf.data.experimental.AUTOTUNE)
    ds = ds.map(lambda ex: tf.parse_single_example(ex, feature_desc),
                num_parallel_calls=tf.data.experimental.AUTOTUNE)
    if self.get_cached_stats(split)["examples"] <= _MAX_EXAMPLES_TO_MEM_CACHE:
      ds = ds.cache()
    return ds 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def build_input(tfrecord_paths):
  """Builds the graph's input.

  Args:
    tfrecord_paths: List of paths to the input TFRecords

  Returns:
    serialized_example_tensor: The next serialized example. String scalar Tensor
    image_tensor: The decoded image of the example. Uint8 tensor,
        shape=[1, None, None,3]
  """
  filename_queue = tf.train.string_input_producer(
      tfrecord_paths, shuffle=False, num_epochs=1)

  tf_record_reader = tf.TFRecordReader()
  _, serialized_example_tensor = tf_record_reader.read(filename_queue)
  features = tf.parse_single_example(
      serialized_example_tensor,
      features={
          standard_fields.TfExampleFields.image_encoded:
              tf.FixedLenFeature([], tf.string),
      })
  encoded_image = features[standard_fields.TfExampleFields.image_encoded]
  image_tensor = tf.image.decode_image(encoded_image, channels=3)
  image_tensor.set_shape([None, None, 3])
  image_tensor = tf.expand_dims(image_tensor, 0)

  return serialized_example_tensor, image_tensor 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def read_single_example(example_string):
  """Parses the record string."""
  return tf.parse_single_example(
      example_string,
      features={
          'image': tf.FixedLenFeature([], dtype=tf.string),
          'label': tf.FixedLenFeature([], tf.int64)
      }) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def _wiki_articles(shard_id, wikis_dir=None):
  """Generates WikipediaArticles from GCS that are part of shard shard_id."""
  if not wikis_dir:
    wikis_dir = WIKI_CONTENT_DIR
  with tf.Graph().as_default():
    dataset = tf.data.TFRecordDataset(
        cc_utils.readahead(
            os.path.join(wikis_dir, WIKI_CONTENT_FILE % shard_id)),
        buffer_size=16 * 1000 * 1000)

    def _parse_example(ex_ser):
      """Parse serialized Example containing Wikipedia article content."""
      features = {
          "url": tf.VarLenFeature(tf.string),
          "title": tf.VarLenFeature(tf.string),
          "section_titles": tf.VarLenFeature(tf.string),
          "section_texts": tf.VarLenFeature(tf.string),
      }
      ex = tf.parse_single_example(ex_ser, features)
      for k in ex.keys():
        ex[k] = ex[k].values
      ex["url"] = ex["url"][0]
      ex["title"] = ex["title"][0]
      return ex

    dataset = dataset.map(_parse_example, num_parallel_calls=32)
    dataset = dataset.prefetch(100)
    record_it = dataset.make_one_shot_iterator().get_next()

    with tf.Session() as sess:
      while True:
        try:
          ex = sess.run(record_it)
        except tf.errors.OutOfRangeError:
          break

        sections = [
            WikipediaSection(title=text_encoder.to_unicode(title),
                             text=text_encoder.to_unicode(text))
            for title, text in zip(ex["section_titles"], ex["section_texts"])
        ]
        yield WikipediaArticle(
            url=text_encoder.to_unicode(ex["url"]),
            title=text_encoder.to_unicode(ex["title"]),
            sections=sections) 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def file_based_input_fn_builder(input_file, seq_length, is_training,
                                drop_remainder, task_name, use_tpu, bsz,
                                multiple=1):
  """Creates an `input_fn` closure to be passed to TPUEstimator."""
  labeltype = tf.float32 if task_name == "sts-b" else tf.int64

  name_to_features = {
      "input_ids": tf.FixedLenFeature([seq_length * multiple], tf.int64),
      "input_mask": tf.FixedLenFeature([seq_length * multiple], tf.int64),
      "segment_ids": tf.FixedLenFeature([seq_length * multiple], tf.int64),
      "label_ids": tf.FixedLenFeature([], labeltype),
      "is_real_example": tf.FixedLenFeature([], tf.int64),
  }

  def _decode_record(record, name_to_features):
    """Decodes a record to a TensorFlow example."""
    example = tf.parse_single_example(record, name_to_features)

    # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
    # So cast all int64 to int32.
    for name in list(example.keys()):
      t = example[name]
      if t.dtype == tf.int64:
        t = tf.to_int32(t)
      example[name] = t

    return example

  def input_fn(params):
    """The actual input function."""
    if use_tpu:
      batch_size = params["batch_size"]
    else:
      batch_size = bsz

    # For training, we want a lot of parallel reading and shuffling.
    # For eval, we want no shuffling and parallel reading doesn't matter.
    d = tf.data.TFRecordDataset(input_file)
    if is_training:
      d = d.repeat()
      d = d.shuffle(buffer_size=100)

    d = d.apply(
        contrib_data.map_and_batch(
            lambda record: _decode_record(record, name_to_features),
            batch_size=batch_size,
            drop_remainder=drop_remainder))

    return d

  return input_fn 

# 需要導入模塊: from tensorflow.compat import v1 [as 別名]
# 或者: from tensorflow.compat.v1 import parse_single_example [as 別名]
def input_fn_builder(input_file, seq_length, is_training,
                     drop_remainder, use_tpu, bsz, is_v2):
  """Creates an `input_fn` closure to be passed to TPUEstimator."""

  name_to_features = {
      "unique_ids": tf.FixedLenFeature([], tf.int64),
      "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
      "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
      "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
  }
  # p_mask is not required for SQuAD v1.1
  if is_v2:
    name_to_features["p_mask"] = tf.FixedLenFeature([seq_length], tf.int64)

  if is_training:
    name_to_features["start_positions"] = tf.FixedLenFeature([], tf.int64)
    name_to_features["end_positions"] = tf.FixedLenFeature([], tf.int64)
    name_to_features["is_impossible"] = tf.FixedLenFeature([], tf.int64)

  def _decode_record(record, name_to_features):
    """Decodes a record to a TensorFlow example."""
    example = tf.parse_single_example(record, name_to_features)

    # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
    # So cast all int64 to int32.
    for name in list(example.keys()):
      t = example[name]
      if t.dtype == tf.int64:
        t = tf.to_int32(t)
      example[name] = t

    return example

  def input_fn(params):
    """The actual input function."""
    if use_tpu:
      batch_size = params["batch_size"]
    else:
      batch_size = bsz

    # For training, we want a lot of parallel reading and shuffling.
    # For eval, we want no shuffling and parallel reading doesn't matter.
    d = tf.data.TFRecordDataset(input_file)
    if is_training:
      d = d.repeat()
      d = d.shuffle(buffer_size=100)

    d = d.apply(
        contrib_data.map_and_batch(
            lambda record: _decode_record(record, name_to_features),
            batch_size=batch_size,
            drop_remainder=drop_remainder))

    return d

  return input_fn