Python tfexample_decoder.Tensor方法代碼示例

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def testDecodeExampleWithFloatTensor(self):
    np_array = np.random.rand(2, 3, 1).astype('f')

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'array': self._EncodedFloatFeature(np_array),
    }))

    serialized_example = example.SerializeToString()

    with self.test_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'array': parsing_ops.FixedLenFeature(np_array.shape, dtypes.float32)
      }
      items_to_handlers = {'array': tfexample_decoder.Tensor('array'),}
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_array] = decoder.decode(serialized_example, ['array'])
      self.assertAllEqual(tf_array.eval(), np_array) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def testDecodeExampleWithInt64Tensor(self):
    np_array = np.random.randint(1, 10, size=(2, 3, 1))

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'array': self._EncodedInt64Feature(np_array),
    }))

    serialized_example = example.SerializeToString()

    with self.test_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'array': parsing_ops.FixedLenFeature(np_array.shape, dtypes.int64)
      }
      items_to_handlers = {'array': tfexample_decoder.Tensor('array'),}
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_array] = decoder.decode(serialized_example, ['array'])
      self.assertAllEqual(tf_array.eval(), np_array) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def testDecodeExampleWithVarLenTensor(self):
    np_array = np.array([[[1], [2], [3]], [[4], [5], [6]]])

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'labels': self._EncodedInt64Feature(np_array),
    }))

    serialized_example = example.SerializeToString()

    with self.test_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64),
      }
      items_to_handlers = {'labels': tfexample_decoder.Tensor('labels'),}
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_labels] = decoder.decode(serialized_example, ['labels'])
      labels = tf_labels.eval()
      self.assertAllEqual(labels, np_array.flatten()) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def testDecodeExampleWithFixLenTensorWithShape(self):
    np_array = np.array([[1, 2, 3], [4, 5, 6]])

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'labels': self._EncodedInt64Feature(np_array),
    }))

    serialized_example = example.SerializeToString()

    with self.test_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'labels':
              parsing_ops.FixedLenFeature(
                  np_array.shape, dtype=dtypes.int64),
      }
      items_to_handlers = {
          'labels': tfexample_decoder.Tensor(
              'labels', shape=np_array.shape),
      }
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_labels] = decoder.decode(serialized_example, ['labels'])
      labels = tf_labels.eval()
      self.assertAllEqual(labels, np_array) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def testDecodeExampleWithStringTensor(self):
    tensor_shape = (2, 3, 1)
    np_array = np.array([[['ab'], ['cd'], ['ef']],
                         [['ghi'], ['jkl'], ['mnop']]])

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'labels': self._BytesFeature(np_array),
    }))

    serialized_example = example.SerializeToString()

    with self.test_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'labels':
              parsing_ops.FixedLenFeature(
                  tensor_shape,
                  dtypes.string,
                  default_value=constant_op.constant(
                      '', shape=tensor_shape, dtype=dtypes.string))
      }
      items_to_handlers = {'labels': tfexample_decoder.Tensor('labels'),}
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_labels] = decoder.decode(serialized_example, ['labels'])
      labels = tf_labels.eval()

      labels = labels.astype(np_array.dtype)
      self.assertTrue(np.array_equal(np_array, labels)) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def testDecodeExampleShapeKeyTensor(self):
    np_image = np.random.rand(2, 3, 1).astype('f')
    np_labels = np.array([[[1], [2], [3]], [[4], [5], [6]]])

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'image': self._EncodedFloatFeature(np_image),
        'image/shape': self._EncodedInt64Feature(np.array(np_image.shape)),
        'labels': self._EncodedInt64Feature(np_labels),
        'labels/shape': self._EncodedInt64Feature(np.array(np_labels.shape)),
    }))

    serialized_example = example.SerializeToString()

    with self.test_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'image': parsing_ops.VarLenFeature(dtype=dtypes.float32),
          'image/shape': parsing_ops.VarLenFeature(dtype=dtypes.int64),
          'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64),
          'labels/shape': parsing_ops.VarLenFeature(dtype=dtypes.int64),
      }
      items_to_handlers = {
          'image':
              tfexample_decoder.Tensor(
                  'image', shape_keys='image/shape'),
          'labels':
              tfexample_decoder.Tensor(
                  'labels', shape_keys='labels/shape'),
      }
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_image, tf_labels] = decoder.decode(serialized_example,
                                             ['image', 'labels'])
      self.assertAllEqual(tf_image.eval(), np_image)
      self.assertAllEqual(tf_labels.eval(), np_labels) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def testDecodeExampleMultiShapeKeyTensor(self):
    np_image = np.random.rand(2, 3, 1).astype('f')
    np_labels = np.array([[[1], [2], [3]], [[4], [5], [6]]])
    height, width, depth = np_labels.shape

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'image': self._EncodedFloatFeature(np_image),
        'image/shape': self._EncodedInt64Feature(np.array(np_image.shape)),
        'labels': self._EncodedInt64Feature(np_labels),
        'labels/height': self._EncodedInt64Feature(np.array([height])),
        'labels/width': self._EncodedInt64Feature(np.array([width])),
        'labels/depth': self._EncodedInt64Feature(np.array([depth])),
    }))

    serialized_example = example.SerializeToString()

    with self.test_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])
      keys_to_features = {
          'image': parsing_ops.VarLenFeature(dtype=dtypes.float32),
          'image/shape': parsing_ops.VarLenFeature(dtype=dtypes.int64),
          'labels': parsing_ops.VarLenFeature(dtype=dtypes.int64),
          'labels/height': parsing_ops.VarLenFeature(dtype=dtypes.int64),
          'labels/width': parsing_ops.VarLenFeature(dtype=dtypes.int64),
          'labels/depth': parsing_ops.VarLenFeature(dtype=dtypes.int64),
      }
      items_to_handlers = {
          'image':
              tfexample_decoder.Tensor(
                  'image', shape_keys='image/shape'),
          'labels':
              tfexample_decoder.Tensor(
                  'labels',
                  shape_keys=['labels/height', 'labels/width', 'labels/depth']),
      }
      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_image, tf_labels] = decoder.decode(serialized_example,
                                             ['image', 'labels'])
      self.assertAllEqual(tf_image.eval(), np_image)
      self.assertAllEqual(tf_labels.eval(), np_labels) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def testDecodeExampleWithTensor(self):
    tensor_shape = (2, 3, 1)
    np_array = np.random.rand(2, 3, 1)

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'image/depth_map': self._EncodedFloatFeature(np_array),
    }))

    serialized_example = example.SerializeToString()

    with self.test_session():
      serialized_example = array_ops.reshape(serialized_example, shape=[])

      keys_to_features = {
          'image/depth_map':
              parsing_ops.FixedLenFeature(
                  tensor_shape,
                  dtypes.float32,
                  default_value=array_ops.zeros(tensor_shape))
      }

      items_to_handlers = {'depth': tfexample_decoder.Tensor('image/depth_map')}

      decoder = tfexample_decoder.TFExampleDecoder(keys_to_features,
                                                   items_to_handlers)
      [tf_depth] = decoder.decode(serialized_example, ['depth'])
      depth = tf_depth.eval()

    self.assertAllClose(np_array, depth) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [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 tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [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 tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def items_to_handlers(self):
    """Items to handlers
    """
    default = {
        'image': tfexample_decoder.Image(
            shape=self.params['image_shape'],
            image_key='image/encoded',
            format_key='image/format'),
        'label': tfexample_decoder.Tensor(tensor_key='image/class'),
        'text': tfexample_decoder.Tensor(tensor_key='image/text'),
        'length': tfexample_decoder.Tensor(tensor_key='image/text_length'),
        'name': tfexample_decoder.Tensor(tensor_key='image/name')
    }
    return default 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [as 別名]
def make_data_provider(self, **kwargs):

    context_keys_to_features = {
        self.params["image_field"]: tf.FixedLenFeature(
            [], dtype=tf.string),
        "image/format": tf.FixedLenFeature(
            [], dtype=tf.string, default_value=self.params["image_format"]),
    }

    sequence_keys_to_features = {
        self.params["caption_ids_field"]: tf.FixedLenSequenceFeature(
            [], dtype=tf.int64),
        self.params["caption_tokens_field"]: tf.FixedLenSequenceFeature(
            [], dtype=tf.string)
    }

    items_to_handlers = {
        "image": tfexample_decoder.Image(
            image_key=self.params["image_field"],
            format_key="image/format",
            channels=3),
        "target_ids":
        tfexample_decoder.Tensor(self.params["caption_ids_field"]),
        "target_tokens":
        tfexample_decoder.Tensor(self.params["caption_tokens_field"]),
        "target_len": tfexample_decoder.ItemHandlerCallback(
            keys=[self.params["caption_tokens_field"]],
            func=lambda x: tf.size(x[self.params["caption_tokens_field"]]))
    }

    decoder = TFSEquenceExampleDecoder(
        context_keys_to_features, sequence_keys_to_features, items_to_handlers)

    dataset = tf.contrib.slim.dataset.Dataset(
        data_sources=self.params["files"],
        reader=tf.TFRecordReader,
        decoder=decoder,
        num_samples=None,
        items_to_descriptions={})

    return tf.contrib.slim.dataset_data_provider.DatasetDataProvider(
        dataset=dataset,
        shuffle=self.params["shuffle"],
        num_epochs=self.params["num_epochs"],
        **kwargs) 

# 需要導入模塊: from tensorflow.contrib.slim.python.slim.data import tfexample_decoder [as 別名]
# 或者: from tensorflow.contrib.slim.python.slim.data.tfexample_decoder import Tensor [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)