Azure-Pipeline: Remove redundant print information
@@ -12,8 +12,7 @@ parser.add_argument("--build_id", type=str, default="0")
parser.add_argument("--stage", type=str, default="collect_log")
parser.add_argument("--gap", type=float, default=0.05)
args = parser.parse_args()
-print('===== collecting log model =======')
-print('build_id: '+args.build_id)
+print('====== collecting model test log =======')
OS='linux'
PLATFORM='icx'
URL ='https://dev.azure.com/lpot-inc/neural-compressor/_build/results?buildId='+args.build_id+'&view=artifacts&pathAsName=false&type=publishedArtifacts'
@@ -79,7 +78,6 @@ def get_model_benchmark_dict_results():
for root, dirs, files in os.walk(args.logs_dir):
for name in files:
file_name = os.path.join(root, name)
- print(file_name)
if "performance-" + precision in name:
for line in open(file_name, "r"):
result = parse_perf_line(line)
@@ -222,14 +220,14 @@ def check_status(precision, precision_upper, check_accuracy=False):
performance_result = get_model_benchmark_dict_results()
current_performance = performance_result.get(precision).get("Value")
refer_performance = refer.get(f"{precision_upper}_Performance")
- print(f"current_performance_data = {current_performance}, refer_performance_data = {refer_performance}")
+ print(f"current_performance_data = {current_performance:.3f}, refer_performance_data = {refer_performance:.3f}")
assert abs(current_performance - refer_performance) / refer_performance <= args.gap
if check_accuracy:
_, accuracy_result = get_model_tuning_dict_results()
current_accuracy = accuracy_result.get(precision).get("Value")
refer_accuracy = refer.get(f"{precision_upper}_Accuracy")
- print(f"current_accuracy_data = {current_accuracy}, refer_accuarcy_data = {refer_accuracy}")
+ print(f"current_accuracy_data = {current_accuracy:.3f}, refer_accuarcy_data = {refer_accuracy:.3f}")
assert abs(current_accuracy - refer_accuracy) / refer_accuracy <= 0.05
