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:
configs = config_util.get_configs_from_pipeline_file(
FLAGS.pipeline_config_path)
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)
pipeline_proto = config_util.create_pipeline_proto_from_configs(configs)
config_text = text_format.MessageToString(pipeline_proto)
tf.gfile.MakeDirs(FLAGS.eval_dir)
with tf.gfile.Open(os.path.join(FLAGS.eval_dir, 'pipeline.config'),
'wb') as f:
f.write(config_text)
model_config = configs['model']
lstm_config = configs['lstm_model']
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
if FLAGS.eval_training_data:
input_config.external_input_reader.CopyFrom(
configs['train_input_config'].external_input_reader)
lstm_config.eval_unroll_length = lstm_config.train_unroll_length
model_fn = functools.partial(
model_builder.build,
model_config=model_config,
lstm_config=lstm_config,
is_training=False)
def get_next(config, model_config, lstm_config, unroll_length):
return seq_dataset_builder.build(config, model_config, lstm_config,
unroll_length)
create_input_dict_fn = functools.partial(get_next, input_config, model_config,
lstm_config,
lstm_config.eval_unroll_length)
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
evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
FLAGS.checkpoint_dir, FLAGS.eval_dir)
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:
configs = config_util.get_configs_from_pipeline_file(
FLAGS.pipeline_config_path)
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)
pipeline_proto = config_util.create_pipeline_proto_from_configs(configs)
config_text = text_format.MessageToString(pipeline_proto)
tf.gfile.MakeDirs(FLAGS.eval_dir)
with tf.gfile.Open(os.path.join(FLAGS.eval_dir, 'pipeline.config'),
'wb') as f:
f.write(config_text)
model_config = configs['model']
lstm_config = configs['lstm_model']
eval_config = configs['eval_config']
input_config = configs['eval_input_config']
if FLAGS.eval_training_data:
input_config.external_input_reader.CopyFrom(
configs['train_input_config'].external_input_reader)
lstm_config.eval_unroll_length = lstm_config.train_unroll_length
model_fn = functools.partial(
model_builder.build,
model_config=model_config,
lstm_config=lstm_config,
is_training=False)
def get_next(config, model_config, lstm_config, unroll_length):
return seq_dataset_builder.build(config, model_config, lstm_config,
unroll_length)
create_input_dict_fn = functools.partial(get_next, input_config, model_config,
lstm_config,
lstm_config.eval_unroll_length)
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
evaluator.evaluate(create_input_dict_fn, model_fn, eval_config, categories,
FLAGS.checkpoint_dir, FLAGS.eval_dir)
def test_create_pipeline_proto_from_configs(self):
"""Tests that proto can be reconstructed from configs dictionary."""
pipeline_config_path = os.path.join(self.get_temp_dir(), "pipeline.config")
pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
pipeline_config.model.ssd.num_classes = 10
pipeline_config.train_config.batch_size = 32
pipeline_config.train_input_reader.label_map_path = "path/to/label_map"
pipeline_config.eval_config.num_examples = 20
pipeline_config.eval_input_reader.add().queue_capacity = 100
pipeline_config.Extensions[
internal_pipeline_pb2.lstm_model].train_unroll_length = 5
pipeline_config.Extensions[
internal_pipeline_pb2.lstm_model].eval_unroll_length = 10
_write_config(pipeline_config, pipeline_config_path)
configs = config_util.get_configs_from_pipeline_file(pipeline_config_path)
pipeline_config_reconstructed = (
config_util.create_pipeline_proto_from_configs(configs))
self.assertEqual(pipeline_config, pipeline_config_reconstructed)
