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update debug for deploment

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Masonmason 2025-07-17 12:05:10 +08:00
parent 45222fdd06
commit be44e6214a
1 changed files with 26 additions and 6 deletions
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32
cluster4npu_ui/core/functions/InferencePipeline.py
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@ -215,22 +215,42 @@ class PipelineStage:
timeout_start = time.time()
while time.time() - timeout_start < 5.0: # 5 second timeout
result = self.multidongle.get_latest_inference_result(timeout=0.1)
# Check if result is not None and not an empty dict/tuple
print(f"[Stage {self.stage_id}] Got result from MultiDongle: {result}")
# Check if result is valid (not None, not (None, None))
if result is not None:
if isinstance(result, dict):
if result: # Non-empty dict
if isinstance(result, tuple) and len(result) == 2:
# Handle tuple results like (probability, result_string)
prob, result_str = result
if prob is not None and result_str is not None:
print(f"[Stage {self.stage_id}] Valid result: prob={prob}, result={result_str}")
inference_result = result
break
else:
print(f"[Stage {self.stage_id}] Invalid tuple result: prob={prob}, result={result_str}")
elif isinstance(result, dict):
if result: # Non-empty dict
print(f"[Stage {self.stage_id}] Valid dict result: {result}")
inference_result = result
break
else:
print(f"[Stage {self.stage_id}] Empty dict result")
else:
# For tuple results like (probability, result_string)
print(f"[Stage {self.stage_id}] Other result type: {type(result)}")
inference_result = result
break
else:
print(f"[Stage {self.stage_id}] No result yet, waiting...")
time.sleep(0.01)
# Check if inference_result is empty (handle both dict and tuple types)
if inference_result is None or (isinstance(inference_result, dict) and not inference_result):
print(f"[Stage {self.stage_id}] Warning: No inference result received")
if (inference_result is None or
(isinstance(inference_result, dict) and not inference_result) or
(isinstance(inference_result, tuple) and (not inference_result or inference_result == (None, None)))):
print(f"[Stage {self.stage_id}] Warning: No inference result received after 5 second timeout")
inference_result = {'probability': 0.0, 'result': 'No Result'}
else:
print(f"[Stage {self.stage_id}] ✅ Successfully received inference result: {inference_result}")
# Step 3: Output postprocessing (inter-stage)
processed_result = inference_result