from model_meta import NETS
import os
import subprocess
import pdb
TEST_IMAGE_PATH = 'data/images/list.txt'
TEST_OUTPUT_PATH = 'data/test_output_trt.txt'
TEST_EXE_PATH = './build/src/test/test_trt'
if __name__ == '__main__':
# delete output file
if os.path.isfile(TEST_OUTPUT_PATH):
os.remove(TEST_OUTPUT_PATH)
for net_name, net_meta in NETS.items():
if 'exclude' in net_meta.keys() and net_meta['exclude'] is True:
continue
args = [
TEST_IMAGE_PATH,
net_meta['plan_filename'],
net_meta['input_name'],
str(net_meta['input_height']),
str(net_meta['input_width']),
net_meta['output_names'][0],
str(net_meta['num_classes']),
net_meta['preprocess_fn'].__name__,
str(50), # numRuns
"float", # dataType
str(32), # maxBatchSize
'high_performance',
'power_saver',
'system_settings',
'sustained_high_performance',
'burst',
'default'])
args = parser.parse_args()
if args.verbose:
logging.basicConfig(level=logging.INFO)
with output_manager(args.output_file, 'w+') as output:
output.write("net_name,throughput\n")
for net_name, net_meta in sorted(NETS.items()):
if 'exclude' in net_meta.keys() and net_meta['exclude'] is True:
logging.info("Skipping {}".format(net_name))
continue
logging.info('Testing %s' % net_name)
throughput = (test_tf_average_throughput(net_meta, args.num_runs)
if args.net_type == 'tf' else
test_trt_average_throughput(net_meta, args.data_type, args.num_runs)
if args.net_type == 'trt' else
test_snpe_average_throughput(net_meta, args.runtime, args.num_runs, args.performance_profile))
csv_result = '{},{}'.format(net_name, throughput)
logging.info(csv_result)
with output_manager(args.output_file, 'a') as output:
output.write(csv_result+'\n')