# ask user to keep the run
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")
default=32)
argument_list.add_input_device_argument("Device for processing inputs.",
default="/cpu:0")
argument_list.add_inference_device_argument("Device for inference.",
default="/gpu:0")
argument_list.add_optimization_device_argument("Device for optimization.",
default="/cpu:0")
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()
# load run
run = Run(run_id=arguments.run)
if not run.open():
logging_error("There is no run '{}'.".format(arguments.run))
# print some information
logging_info("Load run '{}'.".format(arguments.run))
logging_info("Model: {}".format(
run.get_config_value("model", "name")))
logging_info("Dataset: {} {}".format(
arguments.dataset, arguments.dataset_split))
logging_info("Preprocessing parallel calls: {}".format(
arguments.num_parallel_calls))
logging_info("Prefetch buffer size: {}".format(
arguments.prefetch_buffer_size))
logging_info("Batch size: {}".format(
arguments.batch_size))
logging_info("Input device: {}".format(