Python tensorflow.python.ops.lookup_ops.tables_initializer() Examples

def _init_local_init_op(self, local_init_op=USE_DEFAULT):
    """Initializes local_init_op.

    Args:
      local_init_op: `Operation` run for every new supervisor instance. If set
      to USE_DEFAULT, use the first op from the GraphKeys.LOCAL_INIT_OP
      collection. If the collection is empty, create an op that initializes
      all local variables and all tables.
    """
    if local_init_op is Supervisor.USE_DEFAULT:
      local_init_op = self._get_first_op_from_collection(
          ops.GraphKeys.LOCAL_INIT_OP)
      if local_init_op is None:
        op_list = [
            variables.local_variables_initializer(),
            lookup_ops.tables_initializer()
        ]
        if op_list:
          local_init_op = control_flow_ops.group(*op_list)
          ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
    self._local_init_op = local_init_op 

def _init_local_init_op(self, local_init_op=USE_DEFAULT):
    """Initializes local_init_op.

    Args:
      local_init_op: `Operation` run for every new supervisor instance. If set
      to USE_DEFAULT, use the first op from the GraphKeys.LOCAL_INIT_OP
      collection. If the collection is empty, create an op that initializes
      all local variables and all tables.
    """
    if local_init_op is Supervisor.USE_DEFAULT:
      local_init_op = self._get_first_op_from_collection(
          ops.GraphKeys.LOCAL_INIT_OP)
      if local_init_op is None:
        op_list = [
            variables.local_variables_initializer(),
            lookup_ops.tables_initializer()
        ]
        if op_list:
          local_init_op = control_flow_ops.group(*op_list)
          ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
    self._local_init_op = local_init_op 

def testTokenize(self,
                   text_input,
                   expected_tokens,
                   expected_starts,
                   expected_ends):
    hub_module_handle = ("tensorflow_text/python/ops/test_data/"
                         "segmenter_hub_module")
    segmenter = hub_module_tokenizer.HubModuleTokenizer(hub_module_handle)
    tokens, starts, ends = segmenter.tokenize_with_offsets(text_input)
    tokens_no_offset = segmenter.tokenize(text_input)
    self.evaluate(lookup_ops.tables_initializer())
    self.evaluate(variables_lib.global_variables_initializer())
    self.assertAllEqual(expected_tokens, tokens)
    self.assertAllEqual(expected_starts, starts)
    self.assertAllEqual(expected_ends, ends)
    self.assertAllEqual(expected_tokens, tokens_no_offset) 

def test_linear_model(self):
    wire_column = fc.categorical_column_with_hash_bucket('wire', 4)
    self.assertEqual(4, wire_column.num_buckets)
    with ops.Graph().as_default():
      model = linear.LinearModel((wire_column,))
      predictions = model({
          wire_column.name:
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=('marlo', 'skywalker', 'omar'),
                  dense_shape=(2, 2))
      })
      wire_var, bias = model.variables

      self.evaluate(variables_lib.global_variables_initializer())
      self.evaluate(lookup_ops.tables_initializer())

      self.assertAllClose((0.,), self.evaluate(bias))
      self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), self.evaluate(wire_var))
      self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
      self.evaluate(wire_var.assign(((1.,), (2.,), (3.,), (4.,))))
      # 'marlo' -> 3: wire_var[3] = 4
      # 'skywalker' -> 2, 'omar' -> 2: wire_var[2] + wire_var[2] = 3+3 = 6
      self.assertAllClose(((4.,), (6.,)), self.evaluate(predictions)) 

def test_linear_model(self):
    column = fc.categorical_column_with_identity(key='aaa', num_buckets=3)
    self.assertEqual(3, column.num_buckets)
    with ops.Graph().as_default():
      model = linear.LinearModel((column,))
      predictions = model({
          column.name:
            sparse_tensor.SparseTensorValue(
                indices=((0, 0), (1, 0), (1, 1)),
                values=(0, 2, 1),
                dense_shape=(2, 2))
      })
      weight_var, bias = model.variables

      self.evaluate(variables_lib.global_variables_initializer())
      self.evaluate(lookup_ops.tables_initializer())

      self.assertAllClose((0.,), self.evaluate(bias))
      self.assertAllClose(((0.,), (0.,), (0.,)), self.evaluate(weight_var))
      self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
      self.evaluate(weight_var.assign(((1.,), (2.,), (3.,))))
      # weight_var[0] = 1
      # weight_var[2] + weight_var[1] = 3+2 = 5
      self.assertAllClose(((1.,), (5.,)), self.evaluate(predictions)) 

def _export_graph(graph, saver, checkpoint_path, export_dir,
                  default_graph_signature, named_graph_signatures,
                  exports_to_keep):
  """Exports graph via session_bundle, by creating a Session."""
  with graph.as_default():
    with tf_session.Session('') as session:
      variables.local_variables_initializer()
      lookup_ops.tables_initializer()
      saver.restore(session, checkpoint_path)

      export = exporter.Exporter(saver)
      export.init(
          init_op=control_flow_ops.group(
              variables.local_variables_initializer(),
              lookup_ops.tables_initializer()),
          default_graph_signature=default_graph_signature,
          named_graph_signatures=named_graph_signatures,
          assets_collection=ops.get_collection(ops.GraphKeys.ASSET_FILEPATHS))
      return export.export(export_dir, contrib_variables.get_global_step(),
                           session, exports_to_keep=exports_to_keep) 

def main_op():
  """Returns a main op to init variables and tables.

  Returns the main op including the group of ops that initializes all
  variables, initializes local variables and initialize all tables.

  Returns:
    The set of ops to be run as part of the main op upon the load operation.
  """
  init = variables.global_variables_initializer()
  init_local = variables.local_variables_initializer()
  init_tables = lookup_ops.tables_initializer()
  return control_flow_ops.group(init, init_local, init_tables)


# TODO(sukritiramesh): Integrate with Saver for complete restore functionality. 

def main_op():
  """Returns a main op to init variables and tables.

  Returns the main op including the group of ops that initializes all
  variables, initializes local variables and initialize all tables.

  Returns:
    The set of ops to be run as part of the main op upon the load operation.
  """
  init = variables.global_variables_initializer()
  init_local = variables.local_variables_initializer()
  init_tables = lookup_ops.tables_initializer()
  return control_flow_ops.group(init, init_local, init_tables)


# TODO(sukritiramesh): Integrate with Saver for complete restore functionality. 

def _init_local_init_op(self, local_init_op=USE_DEFAULT):
    """Initializes local_init_op.

    Args:
      local_init_op: `Operation` run for every new supervisor instance. If set
      to USE_DEFAULT, use the first op from the GraphKeys.LOCAL_INIT_OP
      collection. If the collection is empty, create an op that initializes
      all local variables and all tables.
    """
    if local_init_op is Supervisor.USE_DEFAULT:
      local_init_op = self._get_first_op_from_collection(
          ops.GraphKeys.LOCAL_INIT_OP)
      if local_init_op is None:
        op_list = [
            variables.local_variables_initializer(),
            lookup_ops.tables_initializer()
        ]
        if op_list:
          local_init_op = control_flow_ops.group(*op_list)
          ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
    self._local_init_op = local_init_op 

def testDecodeObjectLabelWithMapping(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes_text = ['cat', 'dog']
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    self._BytesFeature(encoded_jpeg),
                'image/format':
                    self._BytesFeature('jpeg'),
                'image/object/class/text':
                    self._BytesFeature(bbox_classes_text),
            })).SerializeToString()

    label_map_string = """
      item {
        id:3
        name:'cat'
      }
      item {
        id:1
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)
    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [None])

    with self.test_session() as sess:
      sess.run(tf.tables_initializer())
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([3, 1],
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def testDecodeObjectLabelWithMapping(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes_text = ['cat', 'dog']
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    self._BytesFeature(encoded_jpeg),
                'image/format':
                    self._BytesFeature('jpeg'),
                'image/object/class/text':
                    self._BytesFeature(bbox_classes_text),
            })).SerializeToString()

    label_map_string = """
      item {
        id:3
        name:'cat'
      }
      item {
        id:1
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)
    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [None])

    with self.test_session() as sess:
      sess.run(tf.tables_initializer())
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([3, 1],
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def testDecodeObjectLabelNoText(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes = [1, 2]
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': self._BytesFeature(encoded_jpeg),
        'image/format': self._BytesFeature('jpeg'),
        'image/object/class/label': self._Int64Feature(bbox_classes),
    })).SerializeToString()
    label_map_string = """
      item {
        id:1
        name:'cat'
      }
      item {
        id:2
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)

    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[
        fields.InputDataFields.groundtruth_classes].get_shape().as_list()),
                        [None])

    init = tf.tables_initializer()
    with self.test_session() as sess:
      sess.run(init)
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(bbox_classes,
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def testDecodeExampleWithBranchedLookup(self):

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'image/object/class/text': self._BytesFeatureFromList(
            np.array(['cat', 'dog', 'guinea pig'])),
    }))
    serialized_example = example.SerializeToString()
    # 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
    table = lookup_ops.index_table_from_tensor(
        constant_op.constant(['dog', 'guinea pig', 'cat']))

    with self.test_session() as sess:
      sess.run(lookup_ops.tables_initializer())

      serialized_example = array_ops.reshape(serialized_example, shape=[])

      keys_to_features = {
          'image/object/class/text': parsing_ops.VarLenFeature(dtypes.string),
      }

      items_to_handlers = {
          'labels':
              tf_example_decoder.LookupTensor('image/object/class/text', table),
      }

      decoder = slim_example_decoder.TFExampleDecoder(keys_to_features,
                                                      items_to_handlers)
      obtained_class_ids = decoder.decode(serialized_example)[0].eval()

    self.assertAllClose([2, 0, 1], obtained_class_ids) 

def testDecodeObjectLabelNoText(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes = [1, 2]
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': self._BytesFeature(encoded_jpeg),
        'image/format': self._BytesFeature('jpeg'),
        'image/object/class/label': self._Int64Feature(bbox_classes),
    })).SerializeToString()
    label_map_string = """
      item {
        id:1
        name:'cat'
      }
      item {
        id:2
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)

    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[
        fields.InputDataFields.groundtruth_classes].get_shape().as_list()),
                        [None])

    init = tf.tables_initializer()
    with self.test_session() as sess:
      sess.run(init)
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(bbox_classes,
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def testDecodeObjectLabelUnrecognizedName(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes_text = ['cat', 'cheetah']
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    self._BytesFeature(encoded_jpeg),
                'image/format':
                    self._BytesFeature('jpeg'),
                'image/object/class/text':
                    self._BytesFeature(bbox_classes_text),
            })).SerializeToString()

    label_map_string = """
      item {
        id:2
        name:'cat'
      }
      item {
        id:1
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)
    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [None])

    with self.test_session() as sess:
      sess.run(tf.tables_initializer())
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([2, -1],
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def testDecodeObjectLabelWithMapping(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes_text = ['cat', 'dog']
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    self._BytesFeature(encoded_jpeg),
                'image/format':
                    self._BytesFeature('jpeg'),
                'image/object/class/text':
                    self._BytesFeature(bbox_classes_text),
            })).SerializeToString()

    label_map_string = """
      item {
        id:3
        name:'cat'
      }
      item {
        id:1
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)
    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [None])

    with self.test_session() as sess:
      sess.run(tf.tables_initializer())
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([3, 1],
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def testDecodeExampleWithBranchedLookup(self):

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'image/object/class/text': self._BytesFeatureFromList(
            np.array(['cat', 'dog', 'guinea pig'])),
    }))
    serialized_example = example.SerializeToString()
    # 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
    table = lookup_ops.index_table_from_tensor(
        constant_op.constant(['dog', 'guinea pig', 'cat']))

    with self.test_session() as sess:
      sess.run(lookup_ops.tables_initializer())

      serialized_example = array_ops.reshape(serialized_example, shape=[])

      keys_to_features = {
          'image/object/class/text': parsing_ops.VarLenFeature(dtypes.string),
      }

      items_to_handlers = {
          'labels':
              tf_example_decoder.LookupTensor('image/object/class/text', table),
      }

      decoder = slim_example_decoder.TFExampleDecoder(keys_to_features,
                                                      items_to_handlers)
      obtained_class_ids = decoder.decode(serialized_example)[0].eval()

    self.assertAllClose([2, 0, 1], obtained_class_ids) 

def testDecodeObjectLabelNoText(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes = [1, 2]
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': self._BytesFeature(encoded_jpeg),
        'image/format': self._BytesFeature('jpeg'),
        'image/object/class/label': self._Int64Feature(bbox_classes),
    })).SerializeToString()
    label_map_string = """
      item {
        id:1
        name:'cat'
      }
      item {
        id:2
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)

    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[
        fields.InputDataFields.groundtruth_classes].get_shape().as_list()),
                        [None])

    init = tf.tables_initializer()
    with self.test_session() as sess:
      sess.run(init)
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(bbox_classes,
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def testDecodeObjectLabelUnrecognizedName(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes_text = ['cat', 'cheetah']
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    self._BytesFeature(encoded_jpeg),
                'image/format':
                    self._BytesFeature('jpeg'),
                'image/object/class/text':
                    self._BytesFeature(bbox_classes_text),
            })).SerializeToString()

    label_map_string = """
      item {
        id:2
        name:'cat'
      }
      item {
        id:1
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)
    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [None])

    with self.test_session() as sess:
      sess.run(tf.tables_initializer())
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([2, -1],
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def _default_local_init_op():
    return control_flow_ops.group(variables.local_variables_initializer(),
                                  lookup_ops.tables_initializer()) 

def _initialized_session(config=None):
  sess = session.Session(config=config)
  sess.run(variables_lib.global_variables_initializer())
  sess.run(lookup_ops.tables_initializer())
  return sess 

def DISABLED_test_linear_model(self):
    wire_column = fc.categorical_column_with_vocabulary_file(
        key='wire',
        vocabulary_file=self._wire_vocabulary_file_name,
        vocabulary_size=self._wire_vocabulary_size,
        num_oov_buckets=1)
    self.assertEqual(4, wire_column.num_buckets)
    with ops.Graph().as_default():
      model = linear.LinearModel((wire_column,))
      predictions = model({
          wire_column.name:
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=('marlo', 'skywalker', 'omar'),
                  dense_shape=(2, 2))
      })
      wire_var, bias = model.variables

      self.evaluate(variables_lib.global_variables_initializer())
      self.evaluate(lookup_ops.tables_initializer())

      self.assertAllClose((0.,), self.evaluate(bias))
      self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), self.evaluate(wire_var))
      self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
      self.evaluate(wire_var.assign(((1.,), (2.,), (3.,), (4.,))))
      # 'marlo' -> 2: wire_var[2] = 3
      # 'skywalker' -> 3, 'omar' -> 0: wire_var[3] + wire_var[0] = 4+1 = 5
      self.assertAllClose(((3.,), (5.,)), self.evaluate(predictions)) 

def test_linear_model(self):
    wire_column = fc.categorical_column_with_vocabulary_list(
        key='aaa',
        vocabulary_list=('omar', 'stringer', 'marlo'),
        num_oov_buckets=1)
    self.assertEqual(4, wire_column.num_buckets)
    with ops.Graph().as_default():
      model = linear.LinearModel((wire_column,))
      predictions = model({
          wire_column.name:
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=('marlo', 'skywalker', 'omar'),
                  dense_shape=(2, 2))
      })
      wire_var, bias = model.variables

      self.evaluate(variables_lib.global_variables_initializer())
      self.evaluate(lookup_ops.tables_initializer())

      self.assertAllClose((0.,), self.evaluate(bias))
      self.assertAllClose(((0.,), (0.,), (0.,), (0.,)), self.evaluate(wire_var))
      self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
      self.evaluate(wire_var.assign(((1.,), (2.,), (3.,), (4.,))))
      # 'marlo' -> 2: wire_var[2] = 3
      # 'skywalker' -> 3, 'omar' -> 0: wire_var[3] + wire_var[0] = 4+1 = 5
      self.assertAllClose(((3.,), (5.,)), self.evaluate(predictions)) 

def test_linear_model(self):
    column = fc.weighted_categorical_column(
        categorical_column=fc.categorical_column_with_identity(
            key='ids', num_buckets=3),
        weight_feature_key='values')
    with ops.Graph().as_default():
      model = linear.LinearModel((column,))
      predictions = model({
          'ids':
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=(0, 2, 1),
                  dense_shape=(2, 2)),
          'values':
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=(.5, 1., .1),
                  dense_shape=(2, 2))
      })
      weight_var, bias = model.variables

      self.evaluate(variables_lib.global_variables_initializer())
      self.evaluate(lookup_ops.tables_initializer())

      self.assertAllClose((0.,), self.evaluate(bias))
      self.assertAllClose(((0.,), (0.,), (0.,)), self.evaluate(weight_var))
      self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
      self.evaluate(weight_var.assign(((1.,), (2.,), (3.,))))
      # weight_var[0] * weights[0, 0] = 1 * .5 = .5
      # weight_var[2] * weights[1, 0] + weight_var[1] * weights[1, 1]
      # = 3*1 + 2*.1 = 3+.2 = 3.2
      self.assertAllClose(((.5,), (3.2,)), self.evaluate(predictions)) 

def test_linear_model_mismatched_dense_shape(self):
    column = fc.weighted_categorical_column(
        categorical_column=fc.categorical_column_with_identity(
            key='ids', num_buckets=3),
        weight_feature_key='values')
    with ops.Graph().as_default():
      model = linear.LinearModel((column,))
      predictions = model({
          'ids':
              sparse_tensor.SparseTensorValue(
                  indices=((0, 0), (1, 0), (1, 1)),
                  values=(0, 2, 1),
                  dense_shape=(2, 2)),
          'values': ((.5,), (1.,), (.1,))
      })
      weight_var, bias = model.variables

      self.evaluate(variables_lib.global_variables_initializer())
      self.evaluate(lookup_ops.tables_initializer())

      self.assertAllClose((0.,), self.evaluate(bias))
      self.assertAllClose(((0.,), (0.,), (0.,)), self.evaluate(weight_var))
      self.assertAllClose(((0.,), (0.,)), self.evaluate(predictions))
      self.evaluate(weight_var.assign(((1.,), (2.,), (3.,))))
      # weight_var[0] * weights[0, 0] = 1 * .5 = .5
      # weight_var[2] * weights[1, 0] + weight_var[1] * weights[1, 1]
      # = 3*1 + 2*.1 = 3+.2 = 3.2
      self.assertAllClose(((.5,), (3.2,)), self.evaluate(predictions)) 

def testDecodeObjectLabelUnrecognizedName(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes_text = ['cat', 'cheetah']
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    self._BytesFeature(encoded_jpeg),
                'image/format':
                    self._BytesFeature('jpeg'),
                'image/object/class/text':
                    self._BytesFeature(bbox_classes_text),
            })).SerializeToString()

    label_map_string = """
      item {
        id:2
        name:'cat'
      }
      item {
        id:1
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)
    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [None])

    with self.test_session() as sess:
      sess.run(tf.tables_initializer())
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([2, -1],
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def _get_local_init_op():
  """Returns the local init ops to initialize tables and local variables."""
  local_init_op = _get_first_op_from_collection(
      ops.GraphKeys.LOCAL_INIT_OP)
  if local_init_op is None:
    op_list = [
        variables.local_variables_initializer(),
        lookup_ops.tables_initializer()
    ]
    if op_list:
      local_init_op = control_flow_ops.group(*op_list)
      ops.add_to_collection(ops.GraphKeys.LOCAL_INIT_OP, local_init_op)
  return local_init_op 

def testDecodeExampleWithBranchedLookup(self):

    example = example_pb2.Example(features=feature_pb2.Features(feature={
        'image/object/class/text': self._BytesFeatureFromList(
            np.array(['cat', 'dog', 'guinea pig'])),
    }))
    serialized_example = example.SerializeToString()
    # 'dog' -> 0, 'guinea pig' -> 1, 'cat' -> 2
    table = lookup_ops.index_table_from_tensor(
        constant_op.constant(['dog', 'guinea pig', 'cat']))

    with self.test_session() as sess:
      sess.run(lookup_ops.tables_initializer())

      serialized_example = array_ops.reshape(serialized_example, shape=[])

      keys_to_features = {
          'image/object/class/text': parsing_ops.VarLenFeature(dtypes.string),
      }

      items_to_handlers = {
          'labels':
              tf_example_decoder.LookupTensor('image/object/class/text', table),
      }

      decoder = slim_example_decoder.TFExampleDecoder(keys_to_features,
                                                      items_to_handlers)
      obtained_class_ids = decoder.decode(serialized_example)[0].eval()

    self.assertAllClose([2, 0, 1], obtained_class_ids) 

def testDecodeObjectLabelNoText(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes = [1, 2]
    example = tf.train.Example(features=tf.train.Features(feature={
        'image/encoded': self._BytesFeature(encoded_jpeg),
        'image/format': self._BytesFeature('jpeg'),
        'image/object/class/label': self._Int64Feature(bbox_classes),
    })).SerializeToString()
    label_map_string = """
      item {
        id:1
        name:'cat'
      }
      item {
        id:2
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)

    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[
        fields.InputDataFields.groundtruth_classes].get_shape().as_list()),
                        [None])

    init = tf.tables_initializer()
    with self.test_session() as sess:
      sess.run(init)
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual(bbox_classes,
                        tensor_dict[fields.InputDataFields.groundtruth_classes]) 

def testDecodeObjectLabelUnrecognizedName(self):
    image_tensor = np.random.randint(256, size=(4, 5, 3)).astype(np.uint8)
    encoded_jpeg = self._EncodeImage(image_tensor)
    bbox_classes_text = ['cat', 'cheetah']
    example = tf.train.Example(
        features=tf.train.Features(
            feature={
                'image/encoded':
                    self._BytesFeature(encoded_jpeg),
                'image/format':
                    self._BytesFeature('jpeg'),
                'image/object/class/text':
                    self._BytesFeature(bbox_classes_text),
            })).SerializeToString()

    label_map_string = """
      item {
        id:2
        name:'cat'
      }
      item {
        id:1
        name:'dog'
      }
    """
    label_map_path = os.path.join(self.get_temp_dir(), 'label_map.pbtxt')
    with tf.gfile.Open(label_map_path, 'wb') as f:
      f.write(label_map_string)
    example_decoder = tf_example_decoder.TfExampleDecoder(
        label_map_proto_file=label_map_path)
    tensor_dict = example_decoder.decode(tf.convert_to_tensor(example))

    self.assertAllEqual((tensor_dict[fields.InputDataFields.groundtruth_classes]
                         .get_shape().as_list()), [None])

    with self.test_session() as sess:
      sess.run(tf.tables_initializer())
      tensor_dict = sess.run(tensor_dict)

    self.assertAllEqual([2, -1],
                        tensor_dict[fields.InputDataFields.groundtruth_classes])