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

v0.1.18

Browse Source
This commit is contained in:
wty-yy
2025-12-28 21:25:08 +08:00
parent 9bcee5004a
commit f1040af529
11 changed files with 140 additions and 39 deletions
Download Patch File Download Diff File Expand all files Collapse all files

17
robogauge/tasks/pipeline/multi_pipeline.py
View File

@@ -25,6 +25,7 @@ from robogauge.utils.logger import Logger
from robogauge.utils.process_utils import NoDaemonPool
from robogauge.utils.progress_monitor import report_progress, ProgressTypes, ProgressData
from robogauge.utils.file_utils import compress_directory
from robogauge.tasks.gauge.gauge_configs.terrain_levels_config import SEARCH_LEVELS_TERRAINS
multi_logger = Logger() # MultiPipeline logger
@@ -132,7 +133,7 @@ class MultiPipeline:
""" Process results from all processes and aggregate them. """
multi_logger.info("📊 Aggregating Results from all runs...")
summary = {'success': {}, **self.static_info, 'summary': {}}
summary = {'success': {}, **self.static_info, 'summary': {}, 'quality_score': {}, 'terrain_quality_score': {}}
finish_msg = (
f"""\n{'='*20} Run Finish Summary {'='*20}\n"""
f"""{'Seed':^10}{'Base Mass':^15}{'Friction':^15}{'Status':^10}\n"""
@@ -151,6 +152,7 @@ class MultiPipeline:
multi_logger.error("No results to aggregate.")
return
quality_score, terrain_quality_score = summary['quality_score'], summary['terrain_quality_score']
value_collections = defaultdict(lambda: defaultdict(list))
for result in all_results:
for goal, metrics in result['results'].items():
@@ -159,11 +161,22 @@ class MultiPipeline:
for metric, means in metrics.items():
for mean_name, mean_value in means.items():
value_collections[metric][mean_name].append(float(mean_value.split(' ')[0]))
quality_score[mean_name] = 1
for metric, means in value_collections.items():
summary['summary'][metric] = {}
for mean_name, values in means.items():
summary['summary'][metric][mean_name] = f"{float(np.mean(values)):.4f} ± {float(np.std(values)):.4f}"
v = float(np.mean(values))
summary['summary'][metric][mean_name] = f"{v:.4f} ± {float(np.std(values)):.4f}"
weight = 1
if metric in ['ang_vel_err', 'lin_vel_err']:
weight = 2
quality_score[mean_name] *= min(max(1e-9, v), 1.0) ** weight
for mean_name in quality_score:
quality_score[mean_name] = quality_score[mean_name] ** (1 / 8) # 2 + 2 + 1 * 4
terrain_quality_score[mean_name] = quality_score[mean_name]
if summary['terrain_name'] in SEARCH_LEVELS_TERRAINS:
terrain_quality_score[mean_name] = 0.09 * (summary['terrain_level'] - 1) + 0.19 * quality_score[mean_name]
save_path = multi_logger.log_dir / "aggregated_results.yaml"
with open(save_path, 'w') as file:

40
robogauge/tasks/pipeline/stress_pipeline.py
View File

@@ -27,7 +27,7 @@ from robogauge.utils.logger import Logger
from robogauge.utils.process_utils import NoDaemonPool
from robogauge.utils.progress_monitor import report_progress, ProgressTypes, start_progress_monitor_thread, ProgressData
from robogauge.tasks.pipeline import MultiPipeline, LevelPipeline
from robogauge.tasks.gauge.gauge_configs.terrain_levels_config import TerrainSearchLevelsConfig
from robogauge.tasks.gauge.gauge_configs.terrain_levels_config import SEARCH_LEVELS_TERRAINS
from robogauge.utils.file_utils import compress_directory
stress_logger = Logger() # StressPipeline logger
@@ -57,6 +57,7 @@ def run_pipeline(args, progress_queue, data):
if search is True:
args.goals = GOALS['level_pipeline']
args.spawn_type = "level_search"
level, level_results = LevelPipeline(args, console_output=False, progress_data=progress_data).run()
if level == 0: # no valid level found
report_progress(progress_data, ProgressTypes.FINISH, desc=f"❌ Failed (Lv 0)")
@@ -76,6 +77,7 @@ def run_pipeline(args, progress_queue, data):
args.level = level
args.goals = GOALS['multi_pipeline']
args.spawn_type = "level_eval"
results = {
'success': True,
'results': MultiPipeline(args, console_output=False, progress_data=progress_data).run(),
@@ -113,12 +115,9 @@ class StressPipeline:
### Build worker data ###
workers_data = []
terrain_search_levels_config = TerrainSearchLevelsConfig()
for terrain_name in terrain_names:
search_max_level = True
terrain_level_cfg = getattr(terrain_search_levels_config, terrain_name, None)
assert terrain_level_cfg is not None, f"Terrain '{terrain_name}' not found in TerrainSearchLevelsConfig."
if len(terrain_level_cfg.levels) == 1: # Flattened terrain
if terrain_name not in SEARCH_LEVELS_TERRAINS: # Flattened terrain
search_max_level = False
data = {
'task_robot_model': self.task_robot_model,
@@ -179,8 +178,9 @@ class StressPipeline:
stress_logger.error("No results to aggregate.")
return
summary = {**self.static_info, 'summary': {}, 'final_score': {}}
value_collections = defaultdict(lambda: defaultdict(list))
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
for result in all_results:
terrain_name = result['data']['terrain_name']
@@ -194,21 +194,27 @@ class StressPipeline:
summary[key] = result['results']
for metric, means in result['results']['summary'].items():
for mean_name, mean_value in means.items():
value = float(mean_value.split(' ')[0])
if terrain_level is not None: # level terrain
value = (terrain_level - 1) * 0.1 + value * 0.1
value_collections[metric][mean_name].append(value)
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)
final_metrics = defaultdict(list)
for metric, means in value_collections.items():
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
summary['summary'][metric][mean_name] = f"{float(np.mean(values)):.4f} ± {float(np.std(values)):.4f}"
final_metrics[mean_name].append(np.mean(values))
for mean_name, values in final_metrics.items():
summary['final_score'][mean_name] = float(np.mean(values))
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
robust_score[terrain_name][mean_name] = float(np.mean(values))
robust_scores.append(robust_score[terrain_name][mean_name])
summary['robust_score'] = dict(robust_score)
summary['benchmark_score'] = float(np.mean(robust_scores))
save_path = stress_logger.log_dir / "stress_benchmark_results.yaml"
with open(save_path, 'w') as file: