# Default - 0
randParams         = 0 

# 0: Caffe, 1: TensorFlow, Default - 0
modelType          = 0 

# 0: Fixed quantization By tarininng Framework, 1: Dyanamic quantization by TIDL, Default - 1
quantizationStyle  = 1 

# quantRoundAdd/100 will be added while rounding to integer, Default - 50
quantRoundAdd      = 25

numParamBits       = 12
# 0 : 8bit Unsigned, 1 : 8bit Signed Default - 1
inElementType      = 0 

inputNetFile       = "ONNX_Reg200M_CIFAR\trained\regnetx200mf_cifar_Relu96.prototxt
inputParamsFile       = "ONNX_Reg200M_CIFAR\trained\regnetx200mf_cifar_Relu96.caffemodel"

outputNetFile      = "ONNX_Reg200M_CIFAR\model\tidl_net_reg200cifar_relu96.bin"
outputParamsFile   = "ONNX_Reg200M_CIFAR\model\tidl_param_reg200cifar_relu96.bin"

#inQuantFactor = 65025

#rawSampleInData can be either 0 or 1. Default value is 0. Set it to 0, if the input data is encoded, or set it to 1, if the input is RAW data.
rawSampleInData = 1
preProcType   = 4
sampleInData = "ONNX_Reg200M_CIFAR\y\tidl_image_uint8_32x32.y"
tidlStatsTool = "eve_test_dl_algo.out.exe"
runFullNet = 1
#layersGroupId =    0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	0
#conv2dKernelType = 0	0	0	0	0	0	0	0	0	1	1	1	1	1	0	1	1	0	1	0	1	0	0	1	1	1	1	1	1




