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( arguments.input_device)) logging_info("Inference device: {}".format( arguments.inference_device)) logging_info("Optimization device: {}".format( arguments.optimization_device)) logging_info("Tensorflow verbosity: {}".format(
required=True) 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("Model name: {}".format( arguments.model_name)) logging_info("Tensorflow verbosity: {}".format( arguments.tf_verbosity)) logging_info("Tensorflow minimum log level: {}".format( arguments.tf_min_log_level)) should_continue = query_yes_no("Continue?", default="yes") if not should_continue: exit() # set verbosity of tensorflow tfu_set_logging(arguments.tf_verbosity, min_log_level=arguments.tf_min_log_level)