Using only propagateMcLabels

Author: ChSonnabend

GPU/GPUTracking/Global/GPUChainTrackingClusterizer.cxx

@@ -977,7 +977,7 @@ int32_t GPUChainTracking::RunTPCClusterizer(bool synchronizeOutput)
           GPUTPCNNClusterizer& clustererNNShadow = doGPU ? processorsShadow()->tpcNNClusterer[lane] : clustererNN;
           GPUTPCNNClusterizerHost& nnApplication = nnApplications[lane];
 
-          int withMC = (doGPU && propagateMCLabels);
+          // int withMC = (doGPU && propagateMCLabels);
 
           if (nn_settings.nnClusterizerApplyCfDeconvolution) {
             runKernel<GPUTPCCFDeconvolution>({GetGrid(clusterer.mPmemory->counters.nPositions, lane), {iSector}}, true);
@@ -991,10 +991,10 @@ int32_t GPUChainTracking::RunTPCClusterizer(bool synchronizeOutput)
             size_t iSize = CAMath::Min((uint)clustererNNShadow.mNnClusterizerBatchedMode, (uint)(clusterer.mPmemory->counters.nClusters - batchStart));
 
             // auto start0 = std::chrono::high_resolution_clock::now();
-            runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::fillInputNNSingleElement>({GetGrid(iSize * clustererNNShadow.mNnClusterizerElementSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, withMC, batchStart); // Filling the data
+            runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::fillInputNNSingleElement>({GetGrid(iSize * clustererNNShadow.mNnClusterizerElementSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, propagateMCLabels, batchStart); // Filling the data
 
             if (clustererNNShadow.mNnClusterizerSetDeconvolutionFlags) {
-              runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::publishDeconvolutionFlags>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, withMC, batchStart); // Filling the regression data
+              runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::publishDeconvolutionFlags>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, propagateMCLabels, batchStart); // Filling the regression data
             }
 
             // NN evaluations
@@ -1044,14 +1044,14 @@ int32_t GPUChainTracking::RunTPCClusterizer(bool synchronizeOutput)
 
             // Publishing kernels
             if (nnApplication.mModelClass.getNumOutputNodes()[0][1] == 1) {
-              runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::determineClass1Labels>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceOutputDType, withMC, batchStart); // Assigning class labels
+              runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::determineClass1Labels>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceOutputDType, propagateMCLabels, batchStart); // Assigning class labels
             } else {
-              runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::determineClass2Labels>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceOutputDType, withMC, batchStart); // Assigning class labels
+              runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::determineClass2Labels>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceOutputDType, propagateMCLabels, batchStart); // Assigning class labels
             }
             if (!clustererNNShadow.mNnClusterizerUseCfRegression) {
-              runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::publishClass1Regression>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceOutputDType, withMC, batchStart); // Publishing class 1 regression results
+              runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::publishClass1Regression>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceOutputDType, propagateMCLabels, batchStart); // Publishing class 1 regression results
               if (nnApplication.mModelClass.getNumOutputNodes()[0][1] > 1 && nnApplication.mModelReg2.isInitialized()) {
-                runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::publishClass2Regression>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceOutputDType, withMC, batchStart); // Publishing class 2 regression results
+                runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::publishClass2Regression>({GetGrid(iSize, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceOutputDType, propagateMCLabels, batchStart); // Publishing class 2 regression results
               }
             }
           }
@@ -1061,7 +1061,7 @@ int32_t GPUChainTracking::RunTPCClusterizer(bool synchronizeOutput)
               runKernel<GPUTPCCFDeconvolution>({GetGrid(clusterer.mPmemory->counters.nPositions, lane), {iSector}}, true);
             }
             DoDebugAndDump(RecoStep::TPCClusterFinding, GPUChainTrackingDebugFlags::TPCClustererChargeMap, clusterer, &GPUTPCClusterFinder::DumpChargeMap, *mDebugFile, "Split Charges");
-            runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::runCfClusterizer>({GetGrid(clusterer.mPmemory->counters.nClusters, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, withMC, 0); // Running the CF regression kernel - no batching needed: batchStart = 0
+            runKernel<GPUTPCNNClusterizerKernels, GPUTPCNNClusterizerKernels::runCfClusterizer>({GetGrid(clusterer.mPmemory->counters.nClusters, lane), krnlRunRangeNone}, iSector, clustererNNShadow.mNnInferenceInputDType, propagateMCLabels, 0); // Running the CF regression kernel - no batching needed: batchStart = 0
           }
 #else
           GPUFatal("Project not compiled with neural network clusterization. Aborting.");