@@ -50,7 +50,6 @@ GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::run
5050 GPUTPCCFClusterizer::computeClustersImpl (get_num_groups (0 ), get_local_size (0 ), get_group_id (0 ), get_local_id (0 ), clusterer, clusterer.mPmemory ->fragment , smem_new, chargeMap, clusterer.mPfilteredPeakPositions , clusterer.Param ().rec , CPU_PTR (&labelAcc), clusterer.mPmemory ->counters .nClusters , clusterer.mNMaxClusterPerRow , clusterer.mPclusterInRow , clusterOut, clusterer.mPclusterPosInRow );
5151}
5252
53-
5453template <>
5554GPUdii () void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::fillInputNNCPU>(int32_t nBlocks, int32_t nThreads, int32_t iBlock, int32_t iThread, GPUSharedMemory& smem, processorType& processors, uint8_t sector, int8_t dtype, int8_t withMC, uint32_t batchStart)
5655{
@@ -163,8 +162,7 @@ GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::fil
163162 float index_values[3 ] = {
164163 sector / 36 .f ,
165164 row / 152 .f ,
166- static_cast <float >(pad) / GPUTPCGeometry::NPads (row)
167- };
165+ static_cast <float >(pad) / GPUTPCGeometry::NPads (row)};
168166
169167 if (dtype == 0 ) {
170168 clustererNN.mInputData_16 [write_idx] = (OrtDataType::Float16_t)index_values[data_idx];
@@ -191,7 +189,7 @@ GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::fil
191189 // Optimize 3D index calculation
192190 int32_t row_idx = transient_index / clustererNN.mNnClusterizerFullTimeSize ;
193191 int32_t r_local = row_idx - clustererNN.mNnClusterizerSizeInputRow ;
194- int32_t time_idx = transient_index - row_idx* clustererNN.mNnClusterizerFullTimeSize ;
192+ int32_t time_idx = transient_index - row_idx * clustererNN.mNnClusterizerFullTimeSize ;
195193 int32_t t_local = time_idx - clustererNN.mNnClusterizerSizeInputTime ;
196194 int32_t write_idx = base_idx * clustererNN.mNnClusterizerElementSize + row_idx * clustererNN.mNnClusterizerPadTimeSize + time_idx;
197195
@@ -552,7 +550,7 @@ GPUdii() void GPUTPCNNClusterizerKernels::Thread<GPUTPCNNClusterizerKernels::pub
552550// THe following arithmetic is done because the network is trained with a split between IROC and OROC boundary
553551GPUd () int32_t GPUTPCNNClusterizerKernels::padOffset(int32_t row_ref, int32_t row_current)
554552{
555- if (row_current < 0 || row_current >= o2::tpc::constants::MAXGLOBALPADROW) {
553+ if (row_current < 0 || row_current >= o2::tpc::constants::MAXGLOBALPADROW) {
556554 return 0 ; // Short-circuit for negative rows
557555 } else {
558556 return (int )((GPUTPCGeometry::NPads (row_current) - GPUTPCGeometry::NPads (row_ref)) / 2 );
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