GPU NN Clusterizer: Fix some variable types

Author: davidrohr

GPU/GPUTracking/Definitions/GPUSettingsList.h

@@ -253,40 +253,40 @@ EndConfig()
 
 // Settings steering the processing of NN Clusterization
 BeginSubConfig(GPUSettingsProcessingNNclusterizer, nn, configStandalone.proc, "NN", 0, "Processing settings for neural network clusterizer", proc_nn)
-AddOption(applyNNclusterizer, int, 0, "", 0, "(bool, default = 0), if the neural network clusterizer should be used")
+AddOption(applyNNclusterizer, int32_t, 0, "", 0, "(bool, default = 0), if the neural network clusterizer should be used")
 AddOption(nnInferenceDevice, std::string, "CPU", "", 0, "(std::string) Specify inference device (cpu (default), rocm, cuda)")
-AddOption(nnInferenceDeviceId, unsigned int, 0, "", 0, "(unsigned int) Specify inference device id")
-AddOption(nnInferenceAllocateDevMem, int, 0, "", 0, "(bool, default = 0), if the device memory should be allocated for inference")
+AddOption(nnInferenceDeviceId, uint32_t, 0, "", 0, "(uint32_t) Specify inference device id")
+AddOption(nnInferenceAllocateDevMem, int32_t, 0, "", 0, "(bool, default = 0), if the device memory should be allocated for inference")
 AddOption(nnInferenceInputDType, std::string, "FP32", "", 0, "(std::string) Specify the datatype for which inference is performed (FP32: default, fp16)") // fp32 or fp16
 AddOption(nnInferenceOutputDType, std::string, "FP32", "", 0, "(std::string) Specify the datatype for which inference is performed (fp32: default, fp16)") // fp32 or fp16
-AddOption(nnInferenceIntraOpNumThreads, int, 1, "", 0, "Number of threads used to evaluate one neural network (ONNX: SetIntraOpNumThreads). 0 = auto-detect, can lead to problems on SLURM systems")
-AddOption(nnInferenceInterOpNumThreads, int, 1, "", 0, "Number of threads used to evaluate one neural network (ONNX: SetInterOpNumThreads). 0 = auto-detect, can lead to problems on SLURM systems")
-AddOption(nnInferenceEnableOrtOptimization, unsigned int, 99, "", 0, "Enables graph optimizations in ONNX Runtime. Can be [0, 1, 2, 99] -> see https://github.com/microsoft/onnxruntime/blob/3f71d637a83dc3540753a8bb06740f67e926dc13/include/onnxruntime/core/session/onnxruntime_c_api.h#L347")
-AddOption(nnInferenceUseDeterministicCompute, int, 0, "", 0, "Enables deterministic compute in ONNX Runtime were possible. Can be [0, 1] -> see https://github.com/microsoft/onnxruntime/blob/3b97d79b3c12dbf93aa0d563f345714596dc8ab6/onnxruntime/core/framework/session_options.h#L208")
-AddOption(nnInferenceOrtProfiling, int, 0, "", 0, "Enables profiling of model execution in ONNX Runtime")
+AddOption(nnInferenceIntraOpNumThreads, int32_t, 1, "", 0, "Number of threads used to evaluate one neural network (ONNX: SetIntraOpNumThreads). 0 = auto-detect, can lead to problems on SLURM systems")
+AddOption(nnInferenceInterOpNumThreads, int32_t, 1, "", 0, "Number of threads used to evaluate one neural network (ONNX: SetInterOpNumThreads). 0 = auto-detect, can lead to problems on SLURM systems")
+AddOption(nnInferenceEnableOrtOptimization, uint32_t, 99, "", 0, "Enables graph optimizations in ONNX Runtime. Can be [0, 1, 2, 99] -> see https://github.com/microsoft/onnxruntime/blob/3f71d637a83dc3540753a8bb06740f67e926dc13/include/onnxruntime/core/session/onnxruntime_c_api.h#L347")
+AddOption(nnInferenceUseDeterministicCompute, int32_t, 0, "", 0, "Enables deterministic compute in ONNX Runtime were possible. Can be [0, 1] -> see https://github.com/microsoft/onnxruntime/blob/3b97d79b3c12dbf93aa0d563f345714596dc8ab6/onnxruntime/core/framework/session_options.h#L208")
+AddOption(nnInferenceOrtProfiling, int32_t, 0, "", 0, "Enables profiling of model execution in ONNX Runtime")
 AddOption(nnInferenceOrtProfilingPath, std::string, ".", "", 0, "If nnInferenceOrtProfiling is set, the path to store the profiling data")
-AddOption(nnInferenceVerbosity, int, 2, "", 0, "0: All debugs; 1: Warnings + major debugs; 2: Warnings; >=3: No messages")
-AddOption(nnClusterizerAddIndexData, int, 1, "", 0, "If normalized index data (sector, row, pad), should be appended to the input")
-AddOption(nnClusterizerSizeInputRow, int, 3, "", 0, "Size of the input to the NN (currently calculated as (length-1)/2")
-AddOption(nnClusterizerSizeInputPad, int, 3, "", 0, "Size of the input to the NN (currently calculated as (length-1)/2")
-AddOption(nnClusterizerSizeInputTime, int, 3, "", 0, "Size of the input to the NN (currently calculated as (length-1)/2")
-AddOption(nnClusterizerUseCfRegression, int, 0, "", 0, "(bool, default = false) If true, use the regression from the native clusterizer and not the NN")
-AddOption(nnClusterizerApplyCfDeconvolution, int, 0, "", 0, "Applies the CFDeconvolution kernel before the digits to the network are filled")
-AddOption(nnClusterizerBatchedMode, unsigned int, 1, "", 0, "(int, default = 1) If >1, the NN is evaluated on batched input of size specified in this variable")
-AddOption(nnClusterizerVerbosity, int, -1, "", 0, "(int, default = -1) If >0, logging messages of the clusterizer will be displayed. Higher number = higher verbosity")
-AddOption(nnClusterizerBoundaryFillValue, int, -1, "", 0, "Fill value for the boundary of the input to the NN")
-AddOption(nnClusterizerApplyNoiseSuppression, int, 1, "", 0, "Applies the NoiseSuppression kernel before the digits to the network are filled")
-AddOption(nnClusterizerSetDeconvolutionFlags, int, 1, "", 0, "Runs the deconvolution kernel without overwriting the charge in order to make cluster-to-track attachment identical to heuristic CF")
+AddOption(nnInferenceVerbosity, int32_t, 2, "", 0, "0: All debugs; 1: Warnings + major debugs; 2: Warnings; >=3: No messages")
+AddOption(nnClusterizerAddIndexData, int32_t, 1, "", 0, "If normalized index data (sector, row, pad), should be appended to the input")
+AddOption(nnClusterizerSizeInputRow, int32_t, 3, "", 0, "Size of the input to the NN (currently calculated as (length-1)/2")
+AddOption(nnClusterizerSizeInputPad, int32_t, 3, "", 0, "Size of the input to the NN (currently calculated as (length-1)/2")
+AddOption(nnClusterizerSizeInputTime, int32_t, 3, "", 0, "Size of the input to the NN (currently calculated as (length-1)/2")
+AddOption(nnClusterizerUseCfRegression, int32_t, 0, "", 0, "(bool, default = false) If true, use the regression from the native clusterizer and not the NN")
+AddOption(nnClusterizerApplyCfDeconvolution, int32_t, 0, "", 0, "Applies the CFDeconvolution kernel before the digits to the network are filled")
+AddOption(nnClusterizerBatchedMode, uint32_t, 1, "", 0, "(int, default = 1) If >1, the NN is evaluated on batched input of size specified in this variable")
+AddOption(nnClusterizerVerbosity, int32_t, -1, "", 0, "(int, default = -1) If >0, logging messages of the clusterizer will be displayed. Higher number = higher verbosity")
+AddOption(nnClusterizerBoundaryFillValue, int32_t, -1, "", 0, "Fill value for the boundary of the input to the NN")
+AddOption(nnClusterizerApplyNoiseSuppression, int32_t, 1, "", 0, "Applies the NoiseSuppression kernel before the digits to the network are filled")
+AddOption(nnClusterizerSetDeconvolutionFlags, int32_t, 1, "", 0, "Runs the deconvolution kernel without overwriting the charge in order to make cluster-to-track attachment identical to heuristic CF")
 AddOption(nnClassificationPath, std::string, "network_class.onnx", "", 0, "The classification network path")
 AddOption(nnRegressionPath, std::string, "network_reg.onnx", "", 0, "The regression network path")
 AddOption(nnClassThreshold, float, 0.5, "", 0, "The cutoff at which clusters will be accepted / rejected")
-AddOption(nnSigmoidTrafoClassThreshold, int, 1, "", 0, "If true (default), then the classification threshold is transformed by an inverse sigmoid function. This depends on how the network was trained (with a sigmoid as acitvation function in the last layer or not)")
+AddOption(nnSigmoidTrafoClassThreshold, int32_t, 1, "", 0, "If true (default), then the classification threshold is transformed by an inverse sigmoid function. This depends on how the network was trained (with a sigmoid as acitvation function in the last layer or not)")
 AddOption(nnEvalMode, std::string, "c1:r1", "", 0, "Concatenation of modes, e.g. c1:r1 (classification class 1, regression class 1)")
-AddOption(nnClusterizerUseClassification, int, 1, "", 0, "If 1, the classification output of the network is used to select clusters, else only the regression output is used and no clusters are rejected by classification")
-AddOption(nnClusterizerForceGpuInputFill, int, 0, "", 0, "Forces to use the fillInputNNGPU function")
+AddOption(nnClusterizerUseClassification, int32_t, 1, "", 0, "If 1, the classification output of the network is used to select clusters, else only the regression output is used and no clusters are rejected by classification")
+AddOption(nnClusterizerForceGpuInputFill, int32_t, 0, "", 0, "Forces to use the fillInputNNGPU function")
 // CCDB
-AddOption(nnLoadFromCCDB, int, 0, "", 0, "If 1 networks are fetched from ccdb, else locally")
-AddOption(nnCCDBDumpToFile, int, 0, "", 0, "If 1, additionally dump fetched CCDB networks to nnLocalFolder")
+AddOption(nnLoadFromCCDB, int32_t, 0, "", 0, "If 1 networks are fetched from ccdb, else locally")
+AddOption(nnCCDBDumpToFile, int32_t, 0, "", 0, "If 1, additionally dump fetched CCDB networks to nnLocalFolder")
 AddOption(nnLocalFolder, std::string, ".", "", 0, "Local folder in which the networks will be fetched")
 AddOption(nnCCDBPath, std::string, "Users/c/csonnabe/TPC/Clusterization", "", 0, "Folder path containing the networks")
 AddOption(nnCCDBWithMomentum, std::string, "", "", 0, "Distinguishes between the network with and without momentum output for the regression")