Python dataset.Dataset方法代碼示例

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import dataset [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.dataset import Dataset [as 別名]
def _create_tfrecord_dataset(tmpdir):
  if not gfile.Exists(tmpdir):
    gfile.MakeDirs(tmpdir)

  data_sources = test_utils.create_tfrecord_files(tmpdir, num_files=1)

  keys_to_features = {
      'image/encoded':
          parsing_ops.FixedLenFeature(
              shape=(), dtype=dtypes.string, default_value=''),
      'image/format':
          parsing_ops.FixedLenFeature(
              shape=(), dtype=dtypes.string, default_value='jpeg'),
      'image/class/label':
          parsing_ops.FixedLenFeature(
              shape=[1],
              dtype=dtypes.int64,
              default_value=array_ops.zeros(
                  [1], dtype=dtypes.int64))
  }

  items_to_handlers = {
      'image': tfexample_decoder.Image(),
      'label': tfexample_decoder.Tensor('image/class/label'),
  }

  decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                               items_to_handlers)

  return dataset.Dataset(
      data_sources=data_sources,
      reader=io_ops.TFRecordReader,
      decoder=decoder,
      num_samples=100,
      items_to_descriptions=None) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import dataset [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.dataset import Dataset [as 別名]
def _get_split(file_pattern, num_samples, num_views, image_size, vox_size):
  """Get dataset.Dataset for the given dataset file pattern and properties."""

  # A dictionary from TF-Example keys to tf.FixedLenFeature instance.
  keys_to_features = {
      'image': tf.FixedLenFeature(
          shape=[num_views, image_size, image_size, 3],
          dtype=tf.float32, default_value=None),
      'mask': tf.FixedLenFeature(
          shape=[num_views, image_size, image_size, 1],
          dtype=tf.float32, default_value=None),
      'vox': tf.FixedLenFeature(
          shape=[vox_size, vox_size, vox_size, 1],
          dtype=tf.float32, default_value=None),
  }

  items_to_handler = {
      'image': tfexample_decoder.Tensor(
          'image', shape=[num_views, image_size, image_size, 3]),
      'mask': tfexample_decoder.Tensor(
          'mask', shape=[num_views, image_size, image_size, 1]),
      'vox': tfexample_decoder.Tensor(
          'vox', shape=[vox_size, vox_size, vox_size, 1])
  }

  decoder = tfexample_decoder.TFExampleDecoder(
      keys_to_features, items_to_handler)

  return dataset.Dataset(
      data_sources=file_pattern,
      reader=tf.TFRecordReader,
      decoder=decoder,
      num_samples=num_samples,
      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import dataset [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.dataset import Dataset [as 別名]
def get_split(split_name, dataset_dir=None):
  """Gets a dataset tuple with instructions for reading cifar100.

  Args:
    split_name: A train/test split name.
    dataset_dir: The base directory of the dataset sources.

  Returns:
    A `Dataset` namedtuple. Image tensors are integers in [0, 255].

  Raises:
    ValueError: if `split_name` is not a valid train/test split.
  """
  if split_name not in _SPLITS_TO_SIZES:
    raise ValueError('split name %s was not recognized.' % split_name)

  file_pattern = os.path.join(dataset_dir, _FILE_PATTERN % split_name)

  keys_to_features = {
      'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
      'image/format': tf.FixedLenFeature((), tf.string, default_value=''),
      'image/class/fine_label': tf.FixedLenFeature(
          [1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
      'image/class/coarse_label': tf.FixedLenFeature(
          [1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
  }

  items_to_handlers = {
      'image': tfexample_decoder.Image(shape=[32, 32, 3]),
      'fine_label': tfexample_decoder.Tensor('image/class/fine_label'),
      'coarse_label': tfexample_decoder.Tensor('image/class/coarse_label'),
  }

  decoder = tfexample_decoder.TFExampleDecoder(
      keys_to_features, items_to_handlers)

  return dataset.Dataset(
      data_sources=file_pattern,
      reader=tf.TFRecordReader,
      decoder=decoder,
      num_samples=_SPLITS_TO_SIZES[split_name],
      num_classes=_NUM_CLASSES,
      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import dataset [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.dataset import Dataset [as 別名]
def get_split(split_name, dataset_dir=None):
  """Gets a dataset tuple with instructions for reading cifar10.

  Args:
    split_name: A train/test split name.
    dataset_dir: The base directory of the dataset sources.

  Returns:
    A `Dataset` namedtuple. Image tensors are integers in [0, 255].

  Raises:
    ValueError: if `split_name` is not a valid train/test split.
  """
  if split_name not in _SPLITS_TO_SIZES:
    raise ValueError('split name %s was not recognized.' % split_name)

  if dataset_dir is None:
    dataset_dir = _DATASET_DIR

  file_pattern = os.path.join(dataset_dir, _FILE_PATTERN % split_name)

  keys_to_features = {
      'image/encoded': tf.FixedLenFeature((), tf.string, default_value=''),
      'image/format': tf.FixedLenFeature((), tf.string, default_value=''),
      'image/class/label': tf.FixedLenFeature(
          [1], tf.int64, default_value=tf.zeros([1], dtype=tf.int64)),
  }

  items_to_handlers = {
      'image': tfexample_decoder.Image(shape=[32, 32, 3]),
      'label': tfexample_decoder.Tensor('image/class/label'),
  }

  decoder = tfexample_decoder.TFExampleDecoder(
      keys_to_features, items_to_handlers)

  return dataset.Dataset(
      data_sources=file_pattern,
      reader=tf.TFRecordReader,
      decoder=decoder,
      num_samples=_SPLITS_TO_SIZES[split_name],
      num_classes=_NUM_CLASSES,
      items_to_descriptions=_ITEMS_TO_DESCRIPTIONS)