def get_configs_from_pipeline_file(pipeline_config_path, is_training):
"""Reads model configuration from a pipeline_pb2.NetworkPipelineConfig.
Args:
pipeline_config_path: A path directory to the network pipeline config
is_training: A boolean flag to indicate training stage, used for
creating the checkpoint directory which must be created at the
first training iteration.
Returns:
model_config: A model_pb2.ModelConfig
train_config: A train_pb2.TrainConfig
eval_config: A eval_pb2.EvalConfig
dataset_config: A kitti_dataset_pb2.KittiDatasetConfig
"""
pipeline_config = pipeline_pb2.NetworkPipelineConfig()
with open(pipeline_config_path, 'r') as f:
text_format.Merge(f.read(), pipeline_config) #解析config文件
model_config = pipeline_config.model_config
# Make sure the checkpoint name matches the config filename
config_file_name = \
os.path.split(pipeline_config_path)[1].split('.')[0]
checkpoint_name = model_config.checkpoint_name
if config_file_name != checkpoint_name:
raise ValueError('Config and checkpoint names must match.')
output_root_dir = avod.root_dir() + '/data/outputs/' + checkpoint_name
# Construct paths
paths_config = model_config.paths_config
if not paths_config.checkpoint_dir:
checkpoint_dir = output_root_dir + '/checkpoints'
if is_training:
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
paths_config.checkpoint_dir = checkpoint_dir
if not paths_config.logdir:
paths_config.logdir = output_root_dir + '/logs/'
if not paths_config.pred_dir:
paths_config.pred_dir = output_root_dir + '/predictions'
train_config = pipeline_config.train_config
eval_config = pipeline_config.eval_config
dataset_config = pipeline_config.dataset_config
if is_training:
# Copy the config to the experiments folder
experiment_config_path = output_root_dir + '/' +\
model_config.checkpoint_name
experiment_config_path += '.config'
# Copy this even if the config exists, in case some parameters
# were modified
shutil.copy(pipeline_config_path, experiment_config_path)
return model_config, train_config, eval_config, dataset_config
def setUpClass(cls):
pipeline_config = pipeline_pb2.NetworkPipelineConfig()
dataset_config = pipeline_config.dataset_config
config_path = avod.root_dir() + '/configs/unittest_model.config'
cls.model_config = config_build.get_model_config_from_file(config_path)
dataset_config.MergeFrom(DatasetBuilder.KITTI_UNITTEST)
cls.dataset = DatasetBuilder.build_kitti_dataset(dataset_config)