cyy/RoboGauge
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

v1.0.1

Browse Source
This commit is contained in:
wty-yy
2025-12-30 15:59:07 +08:00
parent 5a5500668b
commit de7d740012
7 changed files with 46 additions and 26 deletions
Download Patch File Download Diff File Expand all files Collapse all files

12
robogauge/tasks/pipeline/stress_pipeline.py
View File

@@ -182,10 +182,12 @@ class StressPipeline:
metric_collections = defaultdict(lambda: defaultdict(list))
terrain_collections = defaultdict(lambda: defaultdict(list))
zero_terrain_count = defaultdict(lambda: 0)
robust_score = summary['robust_score']
for result in all_results:
terrain_name = result['data']['terrain_name']
terrain_level = result['level'] # None, 0, 1, ..., 10
scores[terrain_name] = 0.0
robust_score[terrain_name] = {}
key = f'{terrain_name}_{terrain_level}'
key += f'_baseMass{result["data"]["base_mass"]}_friction{result["data"]["friction"]}'
if terrain_level == 0:
@@ -198,7 +200,8 @@ class StressPipeline:
for mean_name, value_str in means.items():
value = float(value_str.split(' ± ')[0])
metric_collections[metric][mean_name].append(value)
for mean_name, value in result['results']['terrain_quality_score'].items():
for mean_name, value_str in result['results']['summary']['terrain_quality_score'].items():
value = float(value_str.split(' ± ')[0])
terrain_collections[terrain_name][mean_name].append(value)
for metric, means in metric_collections.items():
@@ -207,15 +210,14 @@ class StressPipeline:
values.extend([0.0] * sum(zero_terrain_count.values())) # include zero terrains
summary['summary'][metric][mean_name] = f"{float(np.mean(values)):.4f} ± {float(np.std(values)):.4f}"
robust_score = defaultdict(dict)
robust_scores = []
for terrain_name, means in terrain_collections.items():
for mean_name, values in means.items():
values.extend([0.0] * zero_terrain_count[terrain_name]) # include zero terrains
robust_score[terrain_name][mean_name] = float(np.mean(values))
scores[terrain_name] = robust_score[terrain_name]['mean@50']
robust_scores.append(robust_score[terrain_name]['mean@50'])
summary['robust_score'] = dict(robust_score)
for terrain_name in robust_score:
if len(robust_score[terrain_name]) == 0:
robust_score[terrain_name] = None
summary['benchmark_score'] = float(np.mean(list(scores.values())))
scores['benchmark'] = summary['benchmark_score']