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v0.1.16

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wty-yy
2025-12-27 01:44:09 +08:00
parent dad65a4e27
commit 0a3985445a
5 changed files with 216 additions and 35 deletions
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91
robogauge/tasks/pipeline/stress_pipeline.py
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@@ -8,6 +8,7 @@
@Desc : Stress Pipeline for Robogauge
'''
import yaml
import traceback
import functools
import numpy as np
from tqdm import tqdm
@@ -18,45 +19,57 @@ from collections import defaultdict
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
stress_logger = Logger() # StressPipeline logger
def run_pipeline(args, data):
def run_pipeline(args, progress_queue, data):
args = deepcopy(args)
task_id = data['task_id']
search = data['search_max_level']
task_label = f"[{data['terrain_name']}]"
progress_data = ProgressData(
task_id=task_id,
msg_prefix=task_label + ' ',
progress_queue=progress_queue
)
if search is True:
task_label += f" M:{data['base_mass']} F:{data['friction']}"
progress_data.msg_prefix = task_label + ' '
args.friction = data['friction']
args.frictions = [data['friction']]
args.base_mass = data['base_mass']
args.base_masses = [data['base_mass']]
args.task_name = f"{data['task_robot_model']}.{data['terrain_name']}"
args.experiment_name = f"{args.experiment_name}_{data['terrain_name']}_baseMass{data['base_mass']}_friction{data['friction']}"
args.experiment_name = f"{args.experiment_name}_{data['terrain_name']}_M{data['base_mass']}_F{data['friction']}"
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)")
results = {
'success': False,
'results': level_results,
'data': data,
'level': 0,
}
return results
report_progress(progress_data, ProgressTypes.RESET, total=0, desc=f"✅ Found Lv {level} -> Running")
else:
level = None # flat terrain
args.task_name = f"{data['task_robot_model']}.{data['terrain_name']}"
args.experiment_name = f"{args.experiment_name}_{data['terrain_name']}"
level = None # flat terrain
if search:
level, results = LevelPipeline(args, console_output=False).run()
if level == 0: # no valid level found
results = {
'success': False,
'results': results,
'data': data,
'level': 0,
}
else:
args.level = level
results = {
'success': True,
'results': MultiPipeline(args, console_output=False).run(),
'data': data,
'level': level,
}
args.level = level
results = {
'success': True,
'results': MultiPipeline(args, console_output=False, progress_data=progress_data).run(),
'data': data,
'level': level,
}
report_progress(progress_data, ProgressTypes.FINISH, desc=f"✅ Done (Lv {level})")
return results
class StressPipeline:
@@ -81,9 +94,6 @@ class StressPipeline:
terrain_names = self.args.stress_terrain_names
stress_logger.info(f"🌄 Stress Test Terrain Names: {terrain_names}")
ctx = multiprocessing.get_context('spawn')
worker_func = functools.partial(run_pipeline, self.args)
### Build worker data ###
workers_data = []
terrain_search_levels_config = TerrainSearchLevelsConfig()
@@ -109,13 +119,26 @@ class StressPipeline:
else:
workers_data.append(data)
### Start progress monitor ###
progress_queue, monitor_thread = start_progress_monitor_thread(len(workers_data))
for i, data in enumerate(workers_data):
data['task_id'] = i
### Run and collect results ###
ctx = multiprocessing.get_context('spawn')
worker_func = functools.partial(run_pipeline, self.args, progress_queue)
results_list = []
with NoDaemonPool(processes=self.num_processes, context=ctx) as pool:
iterator = pool.imap_unordered(worker_func, workers_data)
for results in tqdm(iterator, total=len(workers_data), desc="Stress Benchmark"):
results_list.append(results)
self.add_static_info('model_path', results['results'].pop('model_path', None))
try:
with NoDaemonPool(processes=self.num_processes, context=ctx) as pool:
iterator = pool.imap_unordered(worker_func, workers_data)
for results in iterator:
results_list.append(results)
self.add_static_info('model_path', results['results'].pop('model_path', None))
except Exception as e:
stress_logger.error(f"❌ Stress benchmark encountered an error: {e}, {traceback.format_exc()}")
finally:
progress_queue.put(None) # Stop the progress monitor thread
monitor_thread.join()
stress_logger.info("✅ Stress Benchmark Completed.")
stress_results = self.aggregate_results(results_list)
@@ -144,6 +167,7 @@ class StressPipeline:
summary = {**self.static_info, 'summary': {}}
value_collections = defaultdict(lambda: defaultdict(list))
zero_terrain_count = 0
for result in all_results:
terrain_name = result['data']['terrain_name']
terrain_level = result['level'] # None, 0, 1, ..., 10
@@ -151,7 +175,11 @@ class StressPipeline:
if terrain_level is not None:
key += f'_{terrain_level}'
key += f'_baseMass{result["data"]["base_mass"]}_friction{result["data"]["friction"]}'
summary[key] = result['results'] if terrain_level != 0 else None
if terrain_level == 0:
summary[key] = None
zero_terrain_count += 1
continue
summary[key] = result['results']
for metric, means in result['results']['summary'].items():
for mean_name, mean_value in means.items():
@@ -160,6 +188,7 @@ class StressPipeline:
for metric, means in value_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}"
save_path = stress_logger.log_dir / "stress_benchmark_results.yaml"