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

inputNetFile      = "/home/liuyuyuan/caffe-jacinto-models-caffe-0.17/scripts/training/ti-helmet-detection/JDetNet/20191106_11-06_ds_PSP_dsFac_32_hdDS8_1/sparse/deploy.prototxt"
inputParamsFile    = "/home/liuyuyuan/caffe-jacinto-models-caffe-0.17/scripts/training/ti-helmet-detection/JDetNet/20191106_11-06_ds_PSP_dsFac_32_hdDS8_1/sparse/ti-helmet-detection_ssdJacintoNetV2_iter_120000.caffemodel"


outputNetFile      = "..\..\test\testvecs\config\tidl_models\jdetnet\tidl_net_jdetNet_ssd.bin"
outputParamsFile   = "..\..\test\testvecs\config\tidl_models\jdetnet\tidl_param_jdetNet_ssd.bin"

rawSampleInData = 0
preProcType   = 4
sampleInData = "..\..\test\testvecs\input\test1.jpg   #trace_dump_0_512x256.y
tidlStatsTool ="..\quantStatsTool\eve_test_dl_algo.out.exe"
#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	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	2	0
#conv2dKernelType = 0	0	0	0	0	0	0	0	0	0	0	0	0	0	0	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	1	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 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 1 2 2 2 2 0
conv2dKernelType = 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 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 1 1 1 1 1 1 1

