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v1.0.2-rc2; fix robogauge eval return None bug

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wty-yy
2026-03-25 15:32:39 +08:00
parent 090fcc093a
commit d2a16cc075
4 changed files with 117 additions and 40 deletions
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3
UPDATE.md
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@@ -1,3 +1,6 @@
# 20260325
## v1.0.2-rc2
1. 修复robogauge评估中返回None导致的训练中断问题
# 20260126
## v1.0.2-rc1
1. 修改高速移动的训练文件到最终版，删除配置中无用注释

24
rsl_rl/rsl_rl/runners/on_policy_runner.py
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@@ -253,6 +253,7 @@ class OnPolicyRunner:
if self.robogauge_client is None:
return
try:
if it % 500 == 0 or last_model:
# export jit model
jit_dir = os.path.join(self.log_dir, 'jit_models')
@@ -266,25 +267,42 @@ class OnPolicyRunner:
task_name=task_name,
experiment_name=self.cfg["experiment_name"]
)
except Exception as e:
print(f"[WARN] RoboGauge submit failed at step {it}: {e}")
return
check_times = 1
if last_model:
check_times = int(1e9) # keep checking until the last model is evaluated
while check_times > 0:
check_times -= 1
try:
self.robogauge_client.monitor_tasks()
except Exception as e:
print(f"[WARN] RoboGauge monitor failed at step {it}: {e}")
break
results_dir = os.path.join(self.log_dir, 'robogauge_results')
os.makedirs(results_dir, exist_ok=True)
result_received = False
for task_id, resp in self.robogauge_client.response_data.items():
scores = resp['results']['scores']
step = resp['step']
if not isinstance(resp, dict):
print(f"[WARN] RoboGauge returned an invalid response for task {task_id}: {resp}")
continue
results = resp.get('results')
step = resp.get('step', it)
if results is None:
print(f"[WARN] RoboGauge returned empty results for task {task_id} at step {step}.")
continue
scores = results.get('scores')
if scores is None:
print(f"[WARN] RoboGauge results for task {task_id} at step {step} do not contain 'scores'.")
continue
if step == it:
result_received = True
for key, val in scores.items():
self.writer.add_scalar(f'RoboGauge/{key}', val, step)
results_path = os.path.join(results_dir, f'results_{step}.yaml')
with open(results_path, 'w', encoding='utf-8') as f:
yaml.dump(resp['results'], f, allow_unicode=True, sort_keys=False)
yaml.dump(results, f, allow_unicode=True, sort_keys=False)
if last_model and result_received:
print(f"RoboGauge result for step {it} received. Exiting wait loop.")

24
rsl_rl/rsl_rl/runners/on_policy_runner_cts.py
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@@ -298,6 +298,7 @@ class OnPolicyRunnerCTS:
if self.robogauge_client is None:
return
try:
if it % 500 == 0 or last_model:
# export jit model
jit_dir = os.path.join(self.log_dir, 'jit_models')
@@ -311,25 +312,42 @@ class OnPolicyRunnerCTS:
task_name=task_name,
experiment_name=self.cfg["experiment_name"]
)
except Exception as e:
print(f"[WARN] RoboGauge submit failed at step {it}: {e}")
return
check_times = 1
if last_model:
check_times = int(1e9) # keep checking until manually stopped
while check_times > 0:
check_times -= 1
try:
self.robogauge_client.monitor_tasks()
except Exception as e:
print(f"[WARN] RoboGauge monitor failed at step {it}: {e}")
break
results_dir = os.path.join(self.log_dir, 'robogauge_results')
os.makedirs(results_dir, exist_ok=True)
result_received = False
for task_id, resp in self.robogauge_client.response_data.items():
scores = resp['results']['scores']
step = resp['step']
if not isinstance(resp, dict):
print(f"[WARN] RoboGauge returned an invalid response for task {task_id}: {resp}")
continue
results = resp.get('results')
step = resp.get('step', it)
if results is None:
print(f"[WARN] RoboGauge returned empty results for task {task_id} at step {step}.")
continue
scores = results.get('scores')
if scores is None:
print(f"[WARN] RoboGauge results for task {task_id} at step {step} do not contain 'scores'.")
continue
if step == it:
result_received = True
for key, val in scores.items():
self.writer.add_scalar(f'RoboGauge/{key}', val, step)
results_path = os.path.join(results_dir, f'results_{step}.yaml')
with open(results_path, 'w', encoding='utf-8') as f:
yaml.dump(resp['results'], f, allow_unicode=True, sort_keys=False)
yaml.dump(results, f, allow_unicode=True, sort_keys=False)
if last_model and result_received:
print(f"RoboGauge result for step {it} received. Exiting wait loop.")

50
tools/logs_merge.py
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@@ -36,7 +36,39 @@ def fast_read(event_file_path, tag_names):
if value.tag in tag_names:
tag_data[event.step][value.tag] = value.simple_value
return pd.DataFrame(tag_data).T
df = pd.DataFrame(tag_data).T
df.index.name = 'step'
return df
def normalize_tb_df(tb_df):
tb_df = tb_df.copy()
if 'step' not in tb_df.columns:
first_col = tb_df.columns[0] if len(tb_df.columns) > 0 else None
if first_col is not None and str(first_col).startswith('Unnamed:'):
tb_df = tb_df.rename(columns={first_col: 'step'})
elif tb_df.index.name == 'step':
tb_df = tb_df.reset_index()
else:
tb_df = tb_df.reset_index().rename(columns={'index': 'step'})
tb_df['step'] = pd.to_numeric(tb_df['step'], errors='coerce')
tb_df = tb_df.dropna(subset=['step'])
tb_df['step'] = tb_df['step'].astype(int)
return tb_df
def get_tb_value(tb_df, step, candidate_tags):
row = tb_df[tb_df['step'] == step]
if row.empty:
raise KeyError(f"No tensorboard entry found for step={step}.")
for tag in candidate_tags:
if tag not in row.columns:
continue
values = row[tag].dropna().values
if len(values) > 0:
return float(values[0])
raise KeyError(f"No tensorboard value found for step={step} in tags: {candidate_tags}")
class Collector:
def __init__(self, log_dirs):
@@ -58,14 +90,14 @@ class Collector:
self.output_tb = self.output_dir / "tb.csv"
if self.output_tb.exists():
print(f"Loading existing tensorboard data from {self.output_tb}")
self.tb_df = pd.read_csv(self.output_tb)
self.tb_df = normalize_tb_df(pd.read_csv(self.output_tb))
else:
start_time = time.time()
print(f"Start reading tensorboard events at {time.ctime(start_time)}")
self.tb_df = fast_read(str(self.log_dirs.glob("events.out.tfevents.*").__next__()), [
self.tb_df = normalize_tb_df(fast_read(str(self.log_dirs.glob("events.out.tfevents.*").__next__()), [
'Terrain/terrain_level_all', 'Episode/terrain_level_all',
'RoboGauge/benchmark'
])
]))
print(f"Finished reading tensorboard events in {time.time() - start_time:.2f} seconds.")
self.tb_df.to_csv(self.output_tb, index=False)
print(f"Saved tensorboard data to {self.output_tb}")
@@ -109,7 +141,11 @@ class Collector:
self.datas[f'{terrain_name}_mean@25'].append(float(data['robust_score'][terrain_name]['mean@25']))
self.datas[f'{terrain_name}_mean@50'].append(float(data['robust_score'][terrain_name]['mean@50']))
self.datas['terrain_level'].append(float(self.tb_df[self.tb_df['step'] == it]['value'].values[0]))
self.datas['terrain_level'].append(get_tb_value(
self.tb_df,
it,
['Terrain/terrain_level_all', 'Episode/terrain_level_all']
))
df = pd.DataFrame(self.datas)
df.to_csv(self.output_csv, index=False)
print(f"Saved merged results to {self.output_csv}")
@@ -117,6 +153,8 @@ class Collector:
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--log-dirs")
parser.add_argument("--read-robogauge", default=True, type=lambda x: (str(x).lower() in ['true', '1']), help="Whether to read robogauge_results")
args = parser.parse_args()
collector = Collector(args.log_dirs)
# collector.collect()
if args.read_robogauge:
collector.collect()