Python tensorflow.deserialize_many_sparse() Examples

def postbatch_fn( image, width, label, length, text ):
    """Post-batching, postprocessing: packs raw tensors into a dictionary for 
       Dataset's iterator output"""

    # Batching is complete, so now we can re-sparsify our labels for ctc_loss
    label = tf.cast( tf.deserialize_many_sparse( label, tf.int64 ),
                     tf.int32 )
    
    # Format relevant features for estimator ingestion
    features = {
        "image"    : image, 
        "width"    : width,
        "length"   : length,
        "text"     : text
    }

    return features, label 

def testDeserializeFailsInconsistentRank(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input0 = self._SparseTensorPlaceholder()
      sp_input1 = self._SparseTensorPlaceholder()
      input0_val = self._SparseTensorValue_5x6(np.arange(6))
      input1_val = self._SparseTensorValue_1x1x1()
      serialized0 = tf.serialize_sparse(sp_input0)
      serialized1 = tf.serialize_sparse(sp_input1)
      serialized_concat = tf.stack([serialized0, serialized1])

      sp_deserialized = tf.deserialize_many_sparse(
          serialized_concat, dtype=tf.int32)

      with self.assertRaisesOpError(
          r"Inconsistent rank across SparseTensors: rank prior to "
          r"SparseTensor\[1\] was: 3 but rank of SparseTensor\[1\] is: 4"):
        sess.run(
            sp_deserialized, {sp_input0: input0_val, sp_input1: input1_val}) 

def testSerializeDeserializeMany(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input0 = self._SparseTensorValue_5x6(np.arange(6))
      sp_input1 = self._SparseTensorValue_3x4(np.arange(6))
      serialized0 = tf.serialize_sparse(sp_input0)
      serialized1 = tf.serialize_sparse(sp_input1)
      serialized_concat = tf.stack([serialized0, serialized1])

      sp_deserialized = tf.deserialize_many_sparse(
          serialized_concat, dtype=tf.int32)

      combined_indices, combined_values, combined_shape = sess.run(
          sp_deserialized)

      self.assertAllEqual(combined_indices[:6, 0], [0] * 6)  # minibatch 0
      self.assertAllEqual(combined_indices[:6, 1:], sp_input0[0])
      self.assertAllEqual(combined_indices[6:, 0], [1] * 6)  # minibatch 1
      self.assertAllEqual(combined_indices[6:, 1:], sp_input1[0])
      self.assertAllEqual(combined_values[:6], sp_input0[1])
      self.assertAllEqual(combined_values[6:], sp_input1[1])
      self.assertAllEqual(combined_shape, [2, 5, 6]) 

def testFeedSerializeDeserializeMany(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input0 = self._SparseTensorPlaceholder()
      sp_input1 = self._SparseTensorPlaceholder()
      input0_val = self._SparseTensorValue_5x6(np.arange(6))
      input1_val = self._SparseTensorValue_3x4(np.arange(6))
      serialized0 = tf.serialize_sparse(sp_input0)
      serialized1 = tf.serialize_sparse(sp_input1)
      serialized_concat = tf.stack([serialized0, serialized1])

      sp_deserialized = tf.deserialize_many_sparse(
          serialized_concat, dtype=tf.int32)

      combined_indices, combined_values, combined_shape = sess.run(
          sp_deserialized, {sp_input0: input0_val, sp_input1: input1_val})

      self.assertAllEqual(combined_indices[:6, 0], [0] * 6)  # minibatch 0
      self.assertAllEqual(combined_indices[:6, 1:], input0_val[0])
      self.assertAllEqual(combined_indices[6:, 0], [1] * 6)  # minibatch 1
      self.assertAllEqual(combined_indices[6:, 1:], input1_val[0])
      self.assertAllEqual(combined_values[:6], input0_val[1])
      self.assertAllEqual(combined_values[6:], input1_val[1])
      self.assertAllEqual(combined_shape, [2, 5, 6]) 

def testSerializeManyDeserializeManyRoundTrip(self):
    with self.test_session(use_gpu=False) as sess:
      # N == 4 because shape_value == [4, 5]
      indices_value = np.array([[0, 0], [0, 1], [2, 0]], dtype=np.int64)
      values_value = np.array([b"a", b"b", b"c"])
      shape_value = np.array([4, 5], dtype=np.int64)
      sparse_tensor = self._SparseTensorPlaceholder(dtype=tf.string)
      serialized = tf.serialize_many_sparse(sparse_tensor)
      deserialized = tf.deserialize_many_sparse(serialized, dtype=tf.string)
      serialized_value, deserialized_value = sess.run(
          [serialized, deserialized],
          feed_dict={sparse_tensor.indices: indices_value,
                     sparse_tensor.values: values_value,
                     sparse_tensor.shape: shape_value})
      self.assertEqual(serialized_value.shape, (4, 3))
      self.assertAllEqual(deserialized_value.indices, indices_value)
      self.assertAllEqual(deserialized_value.values, values_value)
      self.assertAllEqual(deserialized_value.shape, shape_value) 

def testDeserializeFailsWrongType(self):
    with self.test_session(use_gpu=False) as sess:
      sp_input0 = self._SparseTensorPlaceholder()
      sp_input1 = self._SparseTensorPlaceholder()
      input0_val = self._SparseTensorValue_5x6(np.arange(6))
      input1_val = self._SparseTensorValue_3x4(np.arange(6))
      serialized0 = tf.serialize_sparse(sp_input0)
      serialized1 = tf.serialize_sparse(sp_input1)
      serialized_concat = tf.stack([serialized0, serialized1])

      sp_deserialized = tf.deserialize_many_sparse(
          serialized_concat, dtype=tf.int64)

      with self.assertRaisesOpError(
          r"Requested SparseTensor of type int64 but "
          r"SparseTensor\[0\].values.dtype\(\) == int32"):
        sess.run(
            sp_deserialized, {sp_input0: input0_val, sp_input1: input1_val}) 

def get_inputs(self):
    """
    Return's tensors for inputs, sequence_lengths and labels
    """
    with tf.device("/cpu:0"):
      inputs, sequence_lengths, labels = self.queue.dequeue()
      labels = tf.deserialize_many_sparse(labels, dtype=tf.int32)
    return inputs, sequence_lengths, labels 

def threaded_input_pipeline(base_dir,file_patterns,
                            num_threads=4,
                            batch_size=32,
                            batch_device=None,
                            preprocess_device=None,
                            num_epochs=None):

    queue_capacity = num_threads*batch_size*2
    # Allow a smaller final batch if we are going for a fixed number of epochs
    final_batch = (num_epochs!=None) 

    data_queue = _get_data_queue(base_dir, file_patterns, 
                                 capacity=queue_capacity,
                                 num_epochs=num_epochs)

    # each thread has a subgraph with its own reader (sharing filename queue)
    data_tuples = [] # list of subgraph [image, label, width, text] elements
    with tf.device(preprocess_device):
        for _ in range(num_threads):
            image, width, label, length, text, filename  = _read_word_record(
                data_queue)
            image = _preprocess_image(image) # move after batch?
            data_tuples.append([image, width, label, length, text, filename])

    with tf.device(batch_device): # Create batch queue
        image, width, label, length, text, filename  = tf.train.batch_join( 
            data_tuples, 
            batch_size=batch_size,
            capacity=queue_capacity,
            allow_smaller_final_batch=final_batch,
            dynamic_pad=True)
        label = tf.deserialize_many_sparse(label, tf.int64) # post-batching...
        label = tf.cast(label, tf.int32) # for ctc_loss
    return image, width, label, length, text, filename 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def benchmarkVeryLarge2DFloatSparseTensor(self):
    np.random.seed(127)
    num_elements = 10000
    batch_size = 64
    indices_batch = np.random.randint(
        batch_size, size=num_elements, dtype=np.int64)
    indices_value = np.arange(num_elements, dtype=np.int64)
    indices = np.asarray(
        sorted(zip(indices_batch, indices_value)), dtype=np.int64)
    values = ["feature_value_for_embedding_lookup"] * num_elements
    shape = np.asarray([batch_size, num_elements], dtype=np.int64)
    with tf.Session() as sess:
      with tf.device("/cpu:0"):
        indices = tf.Variable(indices)
        values = tf.Variable(values)
        shape = tf.Variable(shape)
        st = tf.SparseTensor(indices, values, shape)

        st_handles = add_many_sparse_to_tensors_map(st)
        st_roundtrip = take_many_sparse_from_tensors_map(
            sparse_map_op=st_handles.op, sparse_handles=st_handles)
        st_roundtrip_op = st_roundtrip.values.op

        st_serialized = tf.serialize_many_sparse(st)
        st_deserialized = tf.deserialize_many_sparse(
            st_serialized, dtype=values.dtype)
        st_deserialized_op = st_deserialized.values.op

        tf.global_variables_initializer().run()

        st_roundtrip_values = sess.run(st_roundtrip)
        st_deserialized_values = sess.run(st_deserialized)
        np.testing.assert_equal(
            st_roundtrip_values.values, st_deserialized_values.values)
        np.testing.assert_equal(
            st_roundtrip_values.indices, st_deserialized_values.indices)
        np.testing.assert_equal(
            st_roundtrip_values.shape, st_deserialized_values.shape)

        self.run_op_benchmark(
            sess, st_roundtrip_op, min_iters=2000,
            name="benchmark_very_large_2d_float_st_tensor_maps")
        self.run_op_benchmark(
            sess, st_deserialized_op, min_iters=2000,
            name="benchmark_very_large_2d_float_st_serialization") 

def bucketed_input_pipeline(base_dir,file_patterns,
                            num_threads=4,
                            batch_size=32,
                            boundaries=[32, 64, 96, 128, 160, 192, 224, 256],
                            input_device=None,
                            width_threshold=None,
                            length_threshold=None,
                            num_epochs=None):
    """Get input tensors bucketed by image width
    Returns:
      image : float32 image tensor [batch_size 32 ? 1] padded to batch max width
      width : int32 image widths (for calculating post-CNN sequence length)
      label : Sparse tensor with label sequences for the batch
      length : Length of label sequence (text length)
      text  :  Human readable string for the image
      filename : Source file path
    """
    queue_capacity = num_threads*batch_size*2
    # Allow a smaller final batch if we are going for a fixed number of epochs
    final_batch = (num_epochs!=None) 

    data_queue = _get_data_queue(base_dir, file_patterns, 
                                 capacity=queue_capacity,
                                 num_epochs=num_epochs)

    with tf.device(input_device): # Create bucketing batcher
        image, width, label, length, text, filename  = _read_word_record(
            data_queue)
        image = _preprocess_image(image) # move after batch?

        keep_input = _get_input_filter(width, width_threshold,
                                       length, length_threshold)
        data_tuple = [image, label, length, text, filename]
        width,data_tuple = tf.contrib.training.bucket_by_sequence_length(
            input_length=width,
            tensors=data_tuple,
            bucket_boundaries=boundaries,
            batch_size=batch_size,
            capacity=queue_capacity,
            keep_input=keep_input,
            allow_smaller_final_batch=final_batch,
            dynamic_pad=True)
        [image, label, length, text, filename] = data_tuple
        label = tf.deserialize_many_sparse(label, tf.int64) # post-batching...
        label = tf.cast(label, tf.int32) # for ctc_loss
    return image, width, label, length, text, filename 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.image_summary('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.image_summary('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths 

def ImageInput(input_pattern, num_threads, shape, using_ctc, reader=None):
  """Creates an input image tensor from the input_pattern filenames.

  TODO(rays) Expand for 2-d labels, 0-d labels, and logistic targets.
  Args:
    input_pattern:  Filenames of the dataset(s) to read.
    num_threads:    Number of preprocessing threads.
    shape:          ImageShape with the desired shape of the input.
    using_ctc:      Take the unpadded_class labels instead of padded.
    reader:         Function that returns an actual reader to read Examples from
      input files. If None, uses tf.TFRecordReader().
  Returns:
    images:   Float Tensor containing the input image scaled to [-1.28, 1.27].
    heights:  Tensor int64 containing the heights of the images.
    widths:   Tensor int64 containing the widths of the images.
    labels:   Serialized SparseTensor containing the int64 labels.
    sparse_labels:   Serialized SparseTensor containing the int64 labels.
    truths:   Tensor string of the utf8 truth texts.
  Raises:
    ValueError: if the optimizer type is unrecognized.
  """
  data_files = tf.gfile.Glob(input_pattern)
  assert data_files, 'no files found for dataset ' + input_pattern
  queue_capacity = shape.batch_size * num_threads * 2
  filename_queue = tf.train.string_input_producer(
      data_files, capacity=queue_capacity)

  # Create a subgraph with its own reader (but sharing the
  # filename_queue) for each preprocessing thread.
  images_and_label_lists = []
  for _ in range(num_threads):
    image, height, width, labels, text = _ReadExamples(filename_queue, shape,
                                                       using_ctc, reader)
    images_and_label_lists.append([image, height, width, labels, text])
  # Create a queue that produces the examples in batches.
  images, heights, widths, labels, truths = tf.train.batch_join(
      images_and_label_lists,
      batch_size=shape.batch_size,
      capacity=16 * shape.batch_size,
      dynamic_pad=True)
  # Deserialize back to sparse, because the batcher doesn't do sparse.
  labels = tf.deserialize_many_sparse(labels, tf.int64)
  sparse_labels = tf.cast(labels, tf.int32)
  labels = tf.sparse_tensor_to_dense(labels)
  labels = tf.reshape(labels, [shape.batch_size, -1], name='Labels')
  # Crush the other shapes to just the batch dimension.
  heights = tf.reshape(heights, [-1], name='Heights')
  widths = tf.reshape(widths, [-1], name='Widths')
  truths = tf.reshape(truths, [-1], name='Truths')
  # Give the images a nice name as well.
  images = tf.identity(images, name='Images')

  tf.summary.image('Images', images)
  return images, heights, widths, labels, sparse_labels, truths