Python input_reader_pb2.InputReader方法代码示例

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def get_configs_from_pipeline_file():
  """Reads evaluation configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads evaluation config from file specified by pipeline_config_path flag.

  Returns:
    model_config: a model_pb2.DetectionModel
    eval_config: a eval_pb2.EvalConfig
    input_config: a input_reader_pb2.InputReader
  """
  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
    text_format.Merge(f.read(), pipeline_config)

  model_config = pipeline_config.model
  if FLAGS.eval_training_data:
    eval_config = pipeline_config.train_config
  else:
    eval_config = pipeline_config.eval_config
  input_config = pipeline_config.eval_input_reader

  return model_config, eval_config, input_config 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def get_configs_from_pipeline_file():
  """Reads training configuration from a pipeline_pb2.TrainEvalPipelineConfig.

  Reads training config from file specified by pipeline_config_path flag.

  Returns:
    model_config: model_pb2.DetectionModel
    train_config: train_pb2.TrainConfig
    input_config: input_reader_pb2.InputReader
  """
  pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
  with tf.gfile.GFile(FLAGS.pipeline_config_path, 'r') as f:
    text_format.Merge(f.read(), pipeline_config)

  model_config = pipeline_config.model
  train_config = pipeline_config.train_config
  input_config = pipeline_config.train_input_reader

  return model_config, train_config, input_config 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def _update_tf_record_input_path(input_config, input_path):
  """Updates input configuration to reflect a new input path.

  The input_config object is updated in place, and hence not returned.

  Args:
    input_config: A input_reader_pb2.InputReader.
    input_path: A path to data or list of paths.

  Raises:
    TypeError: if input reader type is not `tf_record_input_reader`.
  """
  input_reader_type = input_config.WhichOneof("input_reader")
  if input_reader_type == "tf_record_input_reader":
    input_config.tf_record_input_reader.ClearField("input_path")
    if isinstance(input_path, list):
      input_config.tf_record_input_reader.input_path.extend(input_path)
    else:
      input_config.tf_record_input_reader.input_path.append(input_path)
  else:
    raise TypeError("Input reader type must be `tf_record_input_reader`.") 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def test_sample_all_data(self):
    tf_record_path = self.create_tf_record(num_examples=2)

    input_reader_text_proto = """
      shuffle: false
      num_readers: 1
      sample_1_of_n_examples: 1
      tf_record_input_reader {{
        input_path: '{0}'
      }}
    """.format(tf_record_path)
    input_reader_proto = input_reader_pb2.InputReader()
    text_format.Merge(input_reader_text_proto, input_reader_proto)
    tensor_dict = dataset_builder.make_initializable_iterator(
        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()

    with tf.train.MonitoredSession() as sess:
      output_dict = sess.run(tensor_dict)
      self.assertAllEqual(['0'], output_dict[fields.InputDataFields.source_id])
      output_dict = sess.run(tensor_dict)
      self.assertEquals(['1'], output_dict[fields.InputDataFields.source_id]) 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def test_sample_one_of_n_shards(self):
    tf_record_path = self.create_tf_record(num_examples=4)

    input_reader_text_proto = """
      shuffle: false
      num_readers: 1
      sample_1_of_n_examples: 2
      tf_record_input_reader {{
        input_path: '{0}'
      }}
    """.format(tf_record_path)
    input_reader_proto = input_reader_pb2.InputReader()
    text_format.Merge(input_reader_text_proto, input_reader_proto)
    tensor_dict = dataset_builder.make_initializable_iterator(
        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()

    with tf.train.MonitoredSession() as sess:
      output_dict = sess.run(tensor_dict)
      self.assertAllEqual(['0'], output_dict[fields.InputDataFields.source_id])
      output_dict = sess.run(tensor_dict)
      self.assertEquals(['2'], output_dict[fields.InputDataFields.source_id]) 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def _update_input_path(input_config, input_path):
  """Updates input configuration to reflect a new input path.

  The input_config object is updated in place, and hence not returned.

  Args:
    input_config: A input_reader_pb2.InputReader.
    input_path: A path to data or list of paths.

  Raises:
    TypeError: if input reader type is not `tf_record_input_reader`.
  """
  input_reader_type = input_config.WhichOneof("input_reader")
  if input_reader_type == "tf_record_input_reader":
    input_config.tf_record_input_reader.ClearField("input_path")
    if isinstance(input_path, list):
      input_config.tf_record_input_reader.input_path.extend(input_path)
    else:
      input_config.tf_record_input_reader.input_path.append(input_path)
  else:
    raise TypeError("Input reader type must be `tf_record_input_reader`.") 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def test_build_tf_record_input_reader_and_load_instance_masks(self):
    tf_record_path = self.create_tf_record()

    input_reader_text_proto = """
      shuffle: false
      num_readers: 1
      load_instance_masks: true
      tf_record_input_reader {{
        input_path: '{0}'
      }}
    """.format(tf_record_path)
    input_reader_proto = input_reader_pb2.InputReader()
    text_format.Merge(input_reader_text_proto, input_reader_proto)
    tensor_dict = dataset_util.make_initializable_iterator(
        dataset_builder.build(input_reader_proto, batch_size=1)).get_next()

    sv = tf.train.Supervisor(logdir=self.get_temp_dir())
    with sv.prepare_or_wait_for_session() as sess:
      sv.start_queue_runners(sess)
      output_dict = sess.run(tensor_dict)
    self.assertAllEqual(
        (1, 1, 4, 5),
        output_dict[fields.InputDataFields.groundtruth_instance_masks].shape) 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def test_build_tf_record_input_reader_with_additional_channels(self):
    tf_record_path = self.create_tf_record(has_additional_channels=True)

    input_reader_text_proto = """
      shuffle: false
      num_readers: 1
      tf_record_input_reader {{
        input_path: '{0}'
      }}
    """.format(tf_record_path)
    input_reader_proto = input_reader_pb2.InputReader()
    text_format.Merge(input_reader_text_proto, input_reader_proto)
    tensor_dict = dataset_util.make_initializable_iterator(
        dataset_builder.build(
            input_reader_proto, batch_size=2,
            num_additional_channels=2)).get_next()

    sv = tf.train.Supervisor(logdir=self.get_temp_dir())
    with sv.prepare_or_wait_for_session() as sess:
      sv.start_queue_runners(sess)
      output_dict = sess.run(tensor_dict)

    self.assertEquals((2, 4, 5, 5),
                      output_dict[fields.InputDataFields.image].shape) 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def get_configs_from_multiple_files():
  """Reads evaluation configuration from multiple config files.

  Reads the evaluation config from the following files:
    model_config: Read from --model_config_path
    eval_config: Read from --eval_config_path
    input_config: Read from --input_config_path

  Returns:
    model_config: a model_pb2.DetectionModel
    eval_config: a eval_pb2.EvalConfig
    input_config: a input_reader_pb2.InputReader
  """
  eval_config = eval_pb2.EvalConfig()
  with tf.gfile.GFile(FLAGS.eval_config_path, 'r') as f:
    text_format.Merge(f.read(), eval_config)

  model_config = model_pb2.DetectionModel()
  with tf.gfile.GFile(FLAGS.model_config_path, 'r') as f:
    text_format.Merge(f.read(), model_config)

  input_config = input_reader_pb2.InputReader()
  with tf.gfile.GFile(FLAGS.input_config_path, 'r') as f:
    text_format.Merge(f.read(), input_config)

  return model_config, eval_config, input_config 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def build(input_reader_config):
  """Builds a tensor dictionary based on the InputReader config.

  Args:
    input_reader_config: A input_reader_pb2.InputReader object.

  Returns:
    A tensor dict based on the input_reader_config.

  Raises:
    ValueError: On invalid input reader proto.
  """
  if not isinstance(input_reader_config, input_reader_pb2.InputReader):
    raise ValueError('input_reader_config not of type '
                     'input_reader_pb2.InputReader.')

  if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
    config = input_reader_config.tf_record_input_reader
    _, string_tensor = parallel_reader.parallel_read(
        config.input_path,
        reader_class=tf.TFRecordReader,
        num_epochs=(input_reader_config.num_epochs
                    if input_reader_config.num_epochs else None),
        num_readers=input_reader_config.num_readers,
        shuffle=input_reader_config.shuffle,
        dtypes=[tf.string, tf.string],
        capacity=input_reader_config.queue_capacity,
        min_after_dequeue=input_reader_config.min_after_dequeue)

    return tf_example_decoder.TfExampleDecoder().decode(string_tensor)

  raise ValueError('Unsupported input_reader_config.') 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def test_build_tf_record_input_reader(self):
    tf_record_path = self.create_tf_record()

    input_reader_text_proto = """
      shuffle: false
      num_readers: 1
      tf_record_input_reader {{
        input_path: '{0}'
      }}
    """.format(tf_record_path)
    input_reader_proto = input_reader_pb2.InputReader()
    text_format.Merge(input_reader_text_proto, input_reader_proto)
    tensor_dict = input_reader_builder.build(input_reader_proto)

    sv = tf.train.Supervisor(logdir=self.get_temp_dir())
    with sv.prepare_or_wait_for_session() as sess:
      sv.start_queue_runners(sess)
      output_dict = sess.run(tensor_dict)

    self.assertEquals(
        (4, 5, 3), output_dict[fields.InputDataFields.image].shape)
    self.assertEquals(
        [2], output_dict[fields.InputDataFields.groundtruth_classes])
    self.assertEquals(
        (1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
    self.assertAllEqual(
        [0.0, 0.0, 1.0, 1.0],
        output_dict[fields.InputDataFields.groundtruth_boxes][0]) 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def build(input_reader_config):
  """Builds a tensor dictionary based on the InputReader config.

  Args:
    input_reader_config: A input_reader_pb2.InputReader object.

  Returns:
    A tensor dict based on the input_reader_config.

  Raises:
    ValueError: On invalid input reader proto.
  """
  if not isinstance(input_reader_config, input_reader_pb2.InputReader):
    raise ValueError('input_reader_config not of type '
                     'input_reader_pb2.InputReader.')

  if input_reader_config.WhichOneof('input_reader') == 'tf_record_input_reader':
    config = input_reader_config.tf_record_input_reader
    _, string_tensor = parallel_reader.parallel_read(
        config.input_path,
        reader_class=tf.TFRecordReader,
        num_epochs=(input_reader_config.num_epochs
                    if input_reader_config.num_epochs else None),
        num_readers=input_reader_config.num_readers,
        shuffle=input_reader_config.shuffle,
        dtypes=[tf.string, tf.string],
        capacity=input_reader_config.queue_capacity,
        min_after_dequeue=input_reader_config.min_after_dequeue)

    return tf_example_decoder.TfExampleDecoder().Decode(string_tensor)

  raise ValueError('Unsupported input_reader_config.') 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def test_build_tf_record_input_reader(self):
    tf_record_path = self.create_tf_record()

    input_reader_text_proto = """
      shuffle: false
      num_readers: 1
      tf_record_input_reader {{
        input_path: '{0}'
      }}
    """.format(tf_record_path)
    input_reader_proto = input_reader_pb2.InputReader()
    text_format.Merge(input_reader_text_proto, input_reader_proto)
    tensor_dict = input_reader_builder.build(input_reader_proto)

    with tf.train.MonitoredSession() as sess:
      output_dict = sess.run(tensor_dict)

    self.assertTrue(fields.InputDataFields.groundtruth_instance_masks
                    not in output_dict)
    self.assertEquals(
        (4, 5, 3), output_dict[fields.InputDataFields.image].shape)
    self.assertEquals(
        [2], output_dict[fields.InputDataFields.groundtruth_classes])
    self.assertEquals(
        (1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
    self.assertAllEqual(
        [0.0, 0.0, 1.0, 1.0],
        output_dict[fields.InputDataFields.groundtruth_boxes][0]) 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def test_build_tf_record_input_reader_and_load_instance_masks(self):
    tf_record_path = self.create_tf_record()

    input_reader_text_proto = """
      shuffle: false
      num_readers: 1
      load_instance_masks: true
      tf_record_input_reader {{
        input_path: '{0}'
      }}
    """.format(tf_record_path)
    input_reader_proto = input_reader_pb2.InputReader()
    text_format.Merge(input_reader_text_proto, input_reader_proto)
    tensor_dict = input_reader_builder.build(input_reader_proto)

    with tf.train.MonitoredSession() as sess:
      output_dict = sess.run(tensor_dict)

    self.assertEquals(
        (4, 5, 3), output_dict[fields.InputDataFields.image].shape)
    self.assertEquals(
        [2], output_dict[fields.InputDataFields.groundtruth_classes])
    self.assertEquals(
        (1, 4), output_dict[fields.InputDataFields.groundtruth_boxes].shape)
    self.assertAllEqual(
        [0.0, 0.0, 1.0, 1.0],
        output_dict[fields.InputDataFields.groundtruth_boxes][0])
    self.assertAllEqual(
        (1, 4, 5),
        output_dict[fields.InputDataFields.groundtruth_instance_masks].shape) 

# 需要导入模块: from object_detection.protos import input_reader_pb2 [as 别名]
# 或者: from object_detection.protos.input_reader_pb2 import InputReader [as 别名]
def test_raises_error_with_no_input_paths(self):
    input_reader_text_proto = """
      shuffle: false
      num_readers: 1
      load_instance_masks: true
    """
    input_reader_proto = input_reader_pb2.InputReader()
    text_format.Merge(input_reader_text_proto, input_reader_proto)
    with self.assertRaises(ValueError):
      input_reader_builder.build(input_reader_proto)