def main(_argv):
model_configs = maybe_load_yaml(DEFAULT_INFER_CONFIGS)
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
# replace parameters in configs_file with tf FLAGS
model_configs = update_infer_model_configs(model_configs, FLAGS)
model_dirs = FLAGS.model_dir.strip().split(",")
if len(model_dirs) == 1:
model_configs = deep_merge_dict(model_configs, ModelConfigs.load(model_dirs[0]))
model_configs = update_infer_model_configs(model_configs, FLAGS)
runner = InferExperiment(model_configs=model_configs)
else:
runner = EnsembleExperiment(model_configs=model_configs, model_dirs=model_dirs,
weight_scheme=FLAGS.weight_scheme)
runner.run()
def main(_argv):
model_configs = maybe_load_yaml(DEFAULT_INFER_CONFIGS)
# load flags from config file
model_configs = load_from_config_path(FLAGS.config_paths, model_configs)
# replace parameters in configs_file with tf FLAGS
model_configs = update_infer_model_configs(model_configs, FLAGS)
model_dirs = FLAGS.model_dir.strip().split(",")
if len(model_dirs) == 1:
model_configs = deep_merge_dict(model_configs,
ModelConfigs.load(model_dirs[0]))
runner = InferExperiment(model_configs=model_configs)
else:
runner = EnsembleExperiment(model_configs=model_configs,
model_dirs=model_dirs,
weight_scheme=FLAGS.weight_scheme)
runner.run()
def default_inference_options():
""" Returns a dictionary of default inference options. """
return InferExperiment.default_inference_options()
def default_inference_options():
""" Returns a dictionary of default inference options. """
return InferExperiment.default_inference_options()