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fix: Remove incompatible parameters to match standalone MultiDongle API

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Key fixes:
1. Remove 'block' parameter from put_input() call - not supported in standalone code
2. Remove 'timeout' parameter from get_latest_inference_result() call
3. Improve _has_inference_result() logic to properly detect real inference results
   - Don't count "Processing" or "async" status as valid results
   - Only count actual tuple (prob, result_str) or meaningful dict results
   - Match standalone code behavior for FPS calculation

This should resolve the "unexpected keyword argument" errors and
provide accurate FPS counting like the standalone baseline.

🤖 Generated with [Claude Code](https://claude.ai/code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Masonmason 2025-07-24 11:56:01 +08:00
parent 80275bc774
commit f902659017
1 changed files with 12 additions and 8 deletions
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20
cluster4npu_ui/core/functions/InferencePipeline.py
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@ -168,7 +168,7 @@ class PipelineStage:
print(f"[Stage {self.stage_id}] Worker loop stopped")
def _has_inference_result(self, processed_data) -> bool:
"""Check if processed_data contains a valid inference result"""
"""Check if processed_data contains a valid inference result (like standalone code)"""
if not processed_data:
return False
@ -177,15 +177,19 @@ class PipelineStage:
if hasattr(processed_data, 'stage_results') and processed_data.stage_results:
stage_result = processed_data.stage_results.get(self.stage_id)
if stage_result:
# Check for tuple result (prob, result_str)
# Check for tuple result (prob, result_str) - like standalone code
if isinstance(stage_result, tuple) and len(stage_result) == 2:
prob, result_str = stage_result
return prob is not None and result_str is not None
# Check for dict result with actual inference data
# Check for dict result with actual inference data (not status messages)
elif isinstance(stage_result, dict):
return (stage_result.get("status") != "processing" and
stage_result.get("status") != "async" and
stage_result)
# Don't count "Processing" or "async" status as real results
if stage_result.get("status") in ["processing", "async"]:
return False
# Don't count empty results
if not stage_result or stage_result.get("result") == "Processing":
return False
return True
else:
return stage_result is not None
except Exception:
@ -227,10 +231,10 @@ class PipelineStage:
# Step 3: MultiDongle inference
if isinstance(processed_data, np.ndarray):
self.multidongle.put_input(processed_data, 'BGR565', block=False)
self.multidongle.put_input(processed_data, 'BGR565')
# Get inference result (non-blocking, async pattern like standalone code)
result = self.multidongle.get_latest_inference_result(timeout=0.001)
result = self.multidongle.get_latest_inference_result()
# Process result if available
inference_result = {}