should_keep_run = query_yes_no("Should the run '{}' be kept?".format( os.path.basename(run.base_path)), default="yes") if not should_keep_run: shutil.rmtree(run.base_path) if __name__ == "__main__": # get arguments from console arguments = parse_arguments() # initialize new run run = Run(run_id=None) run.open() run.set_config_value(arguments.input_style_file, "files", "input_style") run.set_config_value(arguments.input_map_file, "files", "input_map") run.set_config_value(arguments.output_map_file, "files", "output_map") run.set_config_value(arguments.output_content_file, "files", "output_content") run.set_config_value(arguments.content_weight, "training", "content_weight") run.set_config_value(arguments.content_layers, "training", "content_layers") run.set_config_value(arguments.style_layers, "training", "style_layers") run.set_config_value(arguments.style_weight, "training", "style_weight") run.set_config_value(arguments.map_channel_weight, "training", "map_channel_weight") run.set_config_value(arguments.num_phases, "training", "num_phases") run.set_config_value(arguments.device, "training", "device") run.set_config_value(arguments.save_interval, "output", "save_interval")
argument_list.add_step_log_interval_argument( "Interval how often step information should be displayed [steps].", default=10) argument_list.add_tf_verbosity_argument("Tensorflow verbosity.", default="info") argument_list.add_tf_min_log_level_argument( "Tensorflow minimum log level.", default=3) arguments = argument_list.parse() # get dataset dataset = get_dataset(arguments.dataset) # initialize new run run = Run(run_id=None) run.open() run.set_config_value(arguments.model, "model", "name") run.set_config_value(arguments.dataset, "model", "dataset") run.set_config_value(arguments.dataset_split, "model", "dataset_split") run.set_config_value(dataset.num_classes + 1, "model", "num_classes") run.set_config_value(arguments.random_seed, "training", "random_seed") run.set_config_value(arguments.op_random_seed, "training", "op_random_seed") run.set_config_value(arguments.num_parallel_calls, "training", "num_parallel_calls") run.set_config_value(arguments.prefetch_buffer_size, "training", "prefetch_buffer_size") run.set_config_value(arguments.shuffle_buffer_size, "training", "shuffle_buffer_size") run.set_config_value(arguments.batch_size, "training", "batch_size") run.set_config_value(arguments.num_steps, "training", "num_steps") run.set_config_value(arguments.learning_rate, "training", "learning_rate")