def main(unused_argv):
assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
assert FLAGS.eval_dir, '`eval_dir` is missing.'
tf.gfile.MakeDirs(FLAGS.eval_dir)
if FLAGS.pipeline_config_path:
configs = config_util.get_configs_from_pipeline_file(
FLAGS.pipeline_config_path)
tf.gfile.Copy(FLAGS.pipeline_config_path,
os.path.join(FLAGS.eval_dir, 'pipeline.config'),
overwrite=True)
else:
configs = config_util.get_configs_from_multiple_files(
model_config_path=FLAGS.model_config_path,
eval_config_path=FLAGS.eval_config_path,
eval_input_config_path=FLAGS.input_config_path)
for name, config in [('model.config', FLAGS.model_config_path),
('eval.config', FLAGS.eval_config_path),
('input.config', FLAGS.input_config_path)]:
tf.gfile.Copy(config,
os.path.join(FLAGS.eval_dir, name),
overwrite=True)
model_config = configs['model']
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
if FLAGS.eval_training_data:
input_config = configs['train_input_config']
model_fn = functools.partial(model_builder.build,
model_config=model_config,
is_training=False)
def get_next(config):
return dataset_builder.make_initializable_iterator(
dataset_builder.build(config)).get_next()
create_input_dict_fn = functools.partial(get_next, 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)
if FLAGS.run_once:
eval_config.max_evals = 1
graph_rewriter_fn = None
if 'graph_rewriter_config' in configs:
graph_rewriter_fn = graph_rewriter_builder.build(
configs['graph_rewriter_config'], is_training=False)
evaluator.evaluate(create_input_dict_fn,
model_fn,
eval_config,
categories,
FLAGS.checkpoint_dir,
FLAGS.eval_dir,
graph_hook_fn=graph_rewriter_fn)
def test_load_bad_label_map(self):
label_map_string = """
item {
id:0
name:'class that should not be indexed at zero'
}
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)
with self.assertRaises(ValueError):
label_map_util.load_labelmap(label_map_path)
def __fetch_category_indices():
dir_path = TFWeaponDetectionAPI.__get_dir_path()
path_to_labels = os.path.join(dir_path + '/data', 'label_map.pbtxt')
class_count = 1
label_map = label_map_util.load_labelmap(path_to_labels)
categories = label_map_util.convert_label_map_to_categories(
label_map, max_num_classes=class_count, use_display_name=True)
category_index = label_map_util.create_category_index(categories)
category_dict = {}
for item in category_index.values():
category_dict[item['id']] = item['name']
category_dict[item['name']] = item['id']
return category_index, category_dict
def __init__(self, model_name=PRETRAINED_ssd_mobilenet_v1_coco_2017_11_17):
self.dir_path = dirname(realpath(__file__))
self.model_path = self.dir_path + '/object_detection/pretrained/'
self.model_file = model_name + '.tar.gz'
self.download_base = 'http://download.tensorflow.org/models/object_detection/'
self.path_to_frozen_graph = self.model_path + model_name + '/frozen_inference_graph.pb'
path_to_labels = os.path.join(self.dir_path + '/object_detection/data',
'mscoco_label_map.pbtxt')
self.class_count = 90
if not path.exists(self.path_to_frozen_graph):
self.__download()
self.__load()
self.label_map = label_map_util.load_labelmap(path_to_labels)
self.categories = label_map_util.convert_label_map_to_categories(
self.label_map,
max_num_classes=self.class_count,
use_display_name=True)
self.category_index = label_map_util.create_category_index(
self.categories)
self.inPipe = Pipe()
self.outPipe = Pipe()