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)