Python object_detection.builders.input_reader_builder.build() Examples

The following are 30 code examples of object_detection.builders.input_reader_builder.build(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module object_detection.builders.input_reader_builder , or try the search function .
Example #1
Source File: eval.py    From DOTA_models with Apache License 2.0 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #2
Source File: eval.py    From MBMD with MIT License 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #3
Source File: eval.py    From MBMD with MIT License 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  model_config, train_config, input_config, eval_config = get_configs_from_pipeline_file()

  model_fn = functools.partial(
      build_man_model,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #4
Source File: eval.py    From HereIsWally with MIT License 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #5
Source File: eval.py    From object_detection_kitti with Apache License 2.0 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #6
Source File: eval.py    From garbage-object-detection-tensorflow with MIT License 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #7
Source File: eval.py    From Hands-On-Machine-Learning-with-OpenCV-4 with MIT License 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #8
Source File: eval.py    From tensorflow with BSD 2-Clause "Simplified" License 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #9
Source File: eval.py    From hands-detection with MIT License 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #10
Source File: eval.py    From moveo_ros with MIT License 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #11
Source File: eval.py    From object_detector_app with MIT License 6 votes vote down vote up 
def main(unused_argv):
  assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
  assert FLAGS.eval_dir, '`eval_dir` is missing.'
  if FLAGS.pipeline_config_path:
    model_config, eval_config, input_config = get_configs_from_pipeline_file()
  else:
    model_config, eval_config, input_config = get_configs_from_multiple_files()

  model_fn = functools.partial(
      model_builder.build,
      model_config=model_config,
      is_training=False)

  create_input_dict_fn = functools.partial(
      input_reader_builder.build,
      input_config)

  label_map = label_map_util.load_labelmap(input_config.label_map_path)
  max_num_classes = max([item.id for item in label_map.item])
  categories = label_map_util.convert_label_map_to_categories(
      label_map, max_num_classes)

  evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
                     FLAGS.checkpoint_dir, FLAGS.eval_dir)
Example #12
Source File: input_reader_builder_test.py    From object_detection_with_tensorflow with MIT License 5 votes vote down vote up 
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)
Example #13
Source File: input_reader_builder_test.py    From hands-detection with MIT License 5 votes vote down vote up 
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])
Example #14
Source File: input_reader_builder_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
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)
Example #15
Source File: input_reader_builder_test.py    From g-tensorflow-models with Apache License 2.0 5 votes vote down vote up 
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])
Example #16
Source File: input_reader_builder_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
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])
Example #17
Source File: input_reader_builder_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
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)
Example #18
Source File: input_reader_builder_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
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)

    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])
    self.assertAllEqual(
        (1, 4, 5),
        output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
Example #19
Source File: input_reader_builder_test.py    From AniSeg with Apache License 2.0 5 votes vote down vote up 
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.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])
Example #20
Source File: input_reader_builder_test.py    From MAX-Object-Detector with Apache License 2.0 5 votes vote down vote up 
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)
Example #21
Source File: input_reader_builder_test.py    From Elphas with Apache License 2.0 5 votes vote down vote up 
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.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])
Example #22
Source File: input_reader_builder_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
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)

    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])
    self.assertAllEqual(
        (1, 4, 5),
        output_dict[fields.InputDataFields.groundtruth_instance_masks].shape)
Example #23
Source File: input_reader_builder_test.py    From object_detection_kitti with Apache License 2.0 5 votes vote down vote up 
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])
Example #24
Source File: input_reader_builder_test.py    From BMW-TensorFlow-Training-GUI with Apache License 2.0 5 votes vote down vote up 
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)
Example #25
Source File: input_reader_builder_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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)
Example #26
Source File: input_reader_builder_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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])
Example #27
Source File: input_reader_builder_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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)
Example #28
Source File: input_reader_builder_test.py    From moveo_ros with MIT License 5 votes vote down vote up 
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])
Example #29
Source File: input_reader_builder_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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)
Example #30
Source File: input_reader_builder_test.py    From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 5 votes vote down vote up 
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])