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v0.1.16; Add StressPipeline

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wty-yy
2025-12-26 14:47:39 +08:00
parent 9d9d83ed9f
commit 65490fe591
31 changed files with 318 additions and 122 deletions
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47
robogauge/tasks/pipeline/multi_pipeline.py
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@@ -21,9 +21,12 @@ from collections import defaultdict
from robogauge.tasks.pipeline.base_pipeline import BasePipeline
from robogauge.utils.task_register import task_register
from robogauge.utils.logger import logger
from robogauge.utils.logger import Logger
multi_logger = Logger() # MultiPipeline logger
def run_single_process(args, data):
from robogauge.utils.logger import logger
seed, base_mass, friction = data
local_args = deepcopy(args)
local_args.seed = seed
@@ -41,6 +44,7 @@ def run_single_process(args, data):
ret = {
'status': 'success',
'results': results,
'data': data,
'model_path': pipeline.robot_cfg.control.model_path,
}
if warning is not None:
@@ -65,7 +69,7 @@ class MultiPipeline:
self.base_masses = args.base_masses
self.num_processes = args.num_processes
self.static_info = {}
logger.create(args.experiment_name+'_multi', args.run_name+'_multi')
multi_logger.create(args.experiment_name+'_multi', args.run_name+'_multi')
def add_static_info(self, key: str, value):
if key not in self.static_info:
@@ -74,55 +78,54 @@ class MultiPipeline:
assert self.static_info[key] == value, f"Static info key '{key}' has conflicting values: {self.static_info[key]} vs {value}"
def run(self):
logger.info(f"🚀 Starting Multi-Process Evaluation with {self.num_processes} processes.")
logger.info(f"🔢 Seeds: {self.seeds}, Frictions: {self.frictions}, Base masses: {self.base_masses}")
multi_logger.info(f"🚀 Starting Multi-Process Evaluation with {self.num_processes} processes.")
multi_logger.info(f"🔢 Seeds: {self.seeds}, Frictions: {self.frictions}, Base masses: {self.base_masses}")
workers_data = list(product(self.seeds, self.base_masses, self.frictions))
ctx = multiprocessing.get_context('spawn')
worker_func = functools.partial(run_single_process, self.args)
results_list = []
success_flags = []
with ctx.Pool(processes=self.num_processes) as pool:
iterator = pool.imap_unordered(worker_func, workers_data)
for results in tqdm(iterator, total=len(workers_data), desc="Evaluation"):
success_flags.append(results['status'] == 'success')
results_list.append(results['results'])
results_list.append(results)
self.add_static_info('model_path', results['model_path'])
self.add_static_info('terrain_name', results['results']['terrain_name'])
self.add_static_info('terrain_level', results['results']['terrain_level'])
if results['status'] != 'success':
data = results['data']
logger.error(f"❌ Process with seed={data[0]}, base_mass={data[1]}, friction={data[2]} failed with error: {results['error_msg']}")
multi_logger.error(f"❌ Process with seed={data[0]}, base_mass={data[1]}, friction={data[2]} failed with error: {results['error_msg']}")
logger.info("✅ Multi-Process Evaluation Completed.")
aggregated_results = self.aggregate_results(results_list, success_flags, workers_data)
multi_logger.info("✅ Multi-Process Evaluation Completed.")
aggregated_results = self.aggregate_results(results_list)
return aggregated_results
def aggregate_results(self, all_results, success_flags, workers_data):
def aggregate_results(self, all_results):
""" Process results from all processes and aggregate them. """
logger.info("📊 Aggregating Results from all runs...")
multi_logger.info("📊 Aggregating Results from all runs...")
summary = {'success': {}, **self.static_info}
finish_msg = (
f"""\n{'='*20} Run Finish Summary {'='*20}\n"""
f"""{'Seed':^10}{'Base Mass':^15}{'Friction':^15}{'Status':^10}\n"""
)
for success, data in zip(success_flags, workers_data):
seed, base_mass, friction = data
all_results = sorted(all_results, key=lambda x: x['data'])
for result in all_results:
seed, base_mass, friction = result['data']
success = result['status'] == 'success'
status_str = "✅" if success else "❌"
finish_msg += f"{seed:^10}{base_mass:^15}{friction:^15}{status_str:^10}\n"
summary['success'][f"Seed_{seed}_BaseMass_{base_mass}_Friction_{friction}"] = True if success else False
finish_msg += f"""{'='*88}"""
logger.info(finish_msg)
multi_logger.info(finish_msg)
if not all_results:
logger.error("No results to aggregate.")
multi_logger.error("No results to aggregate.")
return
value_collections = defaultdict(lambda: defaultdict(list))
for result in all_results:
for goal, metrics in result.items():
for goal, metrics in result['results'].items():
if goal != 'summary':
continue
for metric, means in metrics.items():
@@ -134,13 +137,13 @@ class MultiPipeline:
for mean_name, values in means.items():
summary[metric][mean_name] = f"{float(np.mean(values)):.4f} ± {float(np.std(values)):.4f}"
save_path = logger.log_dir / "aggregated_results.yaml"
save_path = multi_logger.log_dir / "aggregated_results.yaml"
with open(save_path, 'w') as file:
yaml.dump(summary, file, allow_unicode=True, sort_keys=False)
logger.info("✅ Aggregated execution finished.")
logger.info(f"📁 Aggregated results saved to: {save_path}")
multi_logger.info("✅ Aggregated execution finished.")
multi_logger.info(f"📁 Aggregated results saved to: {save_path}")
logger.info(
multi_logger.info(
f"""\n{'='*20} Multi-Run Summary {'='*20}\n"""
f"""{yaml.dump(summary, allow_unicode=True)}"""
f"""{'='*60}"""