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v0.1.18; fix bug

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wty-yy
2025-12-29 01:57:36 +00:00
parent 7a304be263
commit feb9ddf907
3 changed files with 41 additions and 13 deletions
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20
robogauge/tasks/pipeline/stress_pipeline.py
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@@ -181,7 +181,7 @@ class StressPipeline:
summary = {**self.static_info, 'summary': {}, 'robust_score': {}, 'benchmark_score': 0.0}
metric_collections = defaultdict(lambda: defaultdict(list))
terrain_collections = defaultdict(lambda: defaultdict(list))
zero_terrain_count = 0
zero_terrain_count = defaultdict(lambda: 0)
for result in all_results:
terrain_name = result['data']['terrain_name']
terrain_level = result['level'] # None, 0, 1, ..., 10
@@ -189,30 +189,32 @@ class StressPipeline:
key += f'_baseMass{result["data"]["base_mass"]}_friction{result["data"]["friction"]}'
if terrain_level == 0:
summary[key] = None
zero_terrain_count += 1
zero_terrain_count[terrain_name] += 1
continue
summary[key] = result['results']
for metric, means in result['results']['terrain_weighted_summary'].items():
for mean_name, value_str in means.items():
value = float(value_str.split(' ± ')[0])
metric_collections[metric][mean_name].append(value)
if terrain_name != 'stairs_down': # skip stairs_down for metrics means calculation
for metric, means in result['results']['terrain_weighted_summary'].items():
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():
terrain_collections[terrain_name][mean_name].append(value)
for metric, means in metric_collections.items():
summary['summary'][metric] = {}
for mean_name, values in means.items():
values.extend([0.0] * zero_terrain_count) # include zero terrains
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) # include zero terrains
values.extend([0.0] * zero_terrain_count[terrain_name]) # include zero terrains
robust_score[terrain_name][mean_name] = float(np.mean(values))
robust_scores.append(robust_score[terrain_name][mean_name])
if terrain_name != 'stairs_down': # skip stairs_down for benchmark score calculation
robust_scores.append(robust_score[terrain_name]['mean@50'])
summary['robust_score'] = dict(robust_score)
summary['benchmark_score'] = float(np.mean(robust_scores))