(example_serialized)
return features['image/encoded'], label, bbox, features['image/class/text']
Parses an Example proto containing a training example of an image.

  The output of the build_image_data.py image preprocessing script is a dataset
  containing serialized Example protocol buffers. Each Example proto contains
  the following fields:

    image/height: 462
    image/width: 581
    image/colorspace: 'RGB'
    image/channels: 3
    image/class/label: 615
    image/class/synset: 'n03623198'
    image/class/text: 'knee pad'
    image/object/bbox/xmin: 0.1
    image/object/bbox/xmax: 0.9
    image/object/bbox/ymin: 0.2
    image/object/bbox/ymax: 0.6
    image/object/bbox/label: 615
    image/format: 'JPEG'
    image/filename: 'ILSVRC2012_val_00041207.JPEG'
    image/encoded: <JPEG encoded string>

  Args:
    example_serialized: scalar Tensor tf.string containing a serialized
      Example protocol buffer.

  Returns:
    image_buffer: Tensor tf.string containing the contents of a JPEG file.
    label: Tensor tf.int32 containing the label.
    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
      where each coordinate is [0, 1) and the coordinates are arranged as
      [ymin, xmin, ymax, xmax].
    text: Tensor tf.string containing the human-readable label.
Parses an Example proto containing a training example of an image.
Parses an Example proto containing a training example of an image .
def _parse_example_proto(example_serialized):
  """Parses an Example proto containing a training example of an image.

  The output of the build_image_data.py image preprocessing script is a dataset
  containing serialized Example protocol buffers. Each Example proto contains
  the following fields:

    image/height: 462
    image/width: 581
    image/colorspace: 'RGB'
    image/channels: 3
    image/class/label: 615
    image/class/synset: 'n03623198'
    image/class/text: 'knee pad'
    image/object/bbox/xmin: 0.1
    image/object/bbox/xmax: 0.9
    image/object/bbox/ymin: 0.2
    image/object/bbox/ymax: 0.6
    image/object/bbox/label: 615
    image/format: 'JPEG'
    image/filename: 'ILSVRC2012_val_00041207.JPEG'
    image/encoded: <JPEG encoded string>

  Args:
    example_serialized: scalar Tensor tf.string containing a serialized
      Example protocol buffer.

  Returns:
    image_buffer: Tensor tf.string containing the contents of a JPEG file.
    label: Tensor tf.int32 containing the label.
    bbox: 3-D float Tensor of bounding boxes arranged [1, num_boxes, coords]
      where each coordinate is [0, 1) and the coordinates are arranged as
      [ymin, xmin, ymax, xmax].
    text: Tensor tf.string containing the human-readable label.
  """
  # Dense features in Example proto.
  feature_map = {
      'image/encoded': tf.FixedLenFeature([], dtype=tf.string,
                                          default_value=''),
      'image/class/label': tf.FixedLenFeature([1], dtype=tf.int64,
                                              default_value=-1),
      'image/class/text': tf.FixedLenFeature([], dtype=tf.string,
                                             default_value=''),
  }
  sparse_float32 = tf.VarLenFeature(dtype=tf.float32)
  # Sparse features in Example proto.
  feature_map.update(
      {k: sparse_float32 for k in ['image/object/bbox/xmin',
                                   'image/object/bbox/ymin',
                                   'image/object/bbox/xmax',
                                   'image/object/bbox/ymax']})

  features = tf.parse_single_example(example_serialized, feature_map)
  label = tf.cast(features['image/class/label'], dtype=tf.int32)

  xmin = tf.expand_dims(features['image/object/bbox/xmin'].values, 0)
  ymin = tf.expand_dims(features['image/object/bbox/ymin'].values, 0)
  xmax = tf.expand_dims(features['image/object/bbox/xmax'].values, 0)
  ymax = tf.expand_dims(features['image/object/bbox/ymax'].values, 0)

  # Note that we impose an ordering of (y, x) just to make life difficult.
  bbox = tf.concat([ymin, xmin, ymax, xmax], 0)

  # Force the variable number of bounding boxes into the shape
  # [1, num_boxes, coords].
  bbox = tf.expand_dims(bbox, 0)
  bbox = tf.transpose(bbox, [0, 2, 1])

  return features['image/encoded'], label, bbox, features['image/class/text']
def _parse_example_proto ( example_serialized ) : # Dense features in Example proto. feature_map = { 'image/encoded' : tf . FixedLenFeature ( [ ] , dtype = tf . string , default_value = '' ) , 'image/class/label' : tf . FixedLenFeature ( [ 1 ] , dtype = tf . int64 , default_value = - 1 ) , 'image/class/text' : tf . FixedLenFeature ( [ ] , dtype = tf . string , default_value = '' ) , } sparse_float32 = tf . VarLenFeature ( dtype = tf . float32 ) # Sparse features in Example proto. feature_map . update ( { k : sparse_float32 for k in [ 'image/object/bbox/xmin' , 'image/object/bbox/ymin' , 'image/object/bbox/xmax' , 'image/object/bbox/ymax' ] } ) features = tf . parse_single_example ( example_serialized , feature_map ) label = tf . cast ( features [ 'image/class/label' ] , dtype = tf . int32 ) xmin = tf . expand_dims ( features [ 'image/object/bbox/xmin' ] . values , 0 ) ymin = tf . expand_dims ( features [ 'image/object/bbox/ymin' ] . values , 0 ) xmax = tf . expand_dims ( features [ 'image/object/bbox/xmax' ] . values , 0 ) ymax = tf . expand_dims ( features [ 'image/object/bbox/ymax' ] . values , 0 ) # Note that we impose an ordering of (y, x) just to make life difficult. bbox = tf . concat ( [ ymin , xmin , ymax , xmax ] , 0 ) # Force the variable number of bounding boxes into the shape # [1, num_boxes, coords]. bbox = tf . expand_dims ( bbox , 0 ) bbox = tf . transpose ( bbox , [ 0 , 2 , 1 ] ) return features [ 'image/encoded' ] , label , bbox , features [ 'image/class/text' ]