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Original question:

TIDL quantization and the CVPR paper

how about the different of TIDL quantize with caffe-jacinto quantize

Happy_Bobo
Intellectual 325 points

Other Parts Discussed in Thread: AM5728

Hi，

The caffe-jacinto support  do quantize in the inference phase, and TIDL import tool will do quantize when convert the model into *.bin. I want to know the different of them.

I have used the TIDL api to do forward propagate，I did not want to got the output of last layer(InnerProduct ) , but the output of innerProduct's previous layer, it represent the feature of the input.

I am confused that all of them are integer number of 0-255.  But, if I do  propagate with caffe-jacinto  on pc, the output is float number,

so, did I need to do invert quantize to convert the output of TIDL to float number when I compute Euclidean distance of input？

github.com/.../caffe-jacinto

thanks

best regards

  • 0
    TI__Genius 17580 points
    Hi,

    Please refer to FAQ 10 in the user guide to understand output Q format and to convert to output to float point data.
    Also, you can refer to code under "ENABLE_FLOAT_TRACE'" in the tidl_tb.c file (which dupms the output in float )

    Thanks,
    Praveen
  • 0 in reply to Praveen Eppa1
    Intellectual 325 points

    Hi Praveen，

    thanks for your reply, but I don't find the FAQ10, would you please give me a link ?

    thanks

    best regards

  • 0 in reply to Happy_Bobo
    TI__Genius 17580 points

    Hi,

    There is a user guide (TIDeepLearningLibrary_UserGuide.pdf) in "ti_dl\docs" folder. In that go to section 6 FAQ, and in that refer to Q10.

    Thanks,

    Praveen  

  • 0 in reply to Praveen Eppa1
    Intellectual 325 points

    Hi Praveen,

    I have seen the FAQ10, but I have some question about it.

    I get the feature_map of image is signed float when I did propagate on pc with quantize:true in deploy.prototxt,  but unsigned char feature_map on board with TIDL ,

    even I divide the scale factor, the feature_map will not convert to signed float.

    what's more, this is my convert log, the out Q of last layer is 604, so my scale factor is 604/256 = 2.3593  ??

    Fullscreen convert_log.txt Download 
    已复制         1 个文件。

Processing config file .\tempDir\qunat_stats_config.txt !
  0, TIDL_DataLayer                ,  0,  -1 ,  1 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  0 ,    0 ,    0 ,    0 ,    0 ,    1 ,    3 ,  112 ,   96 ,
  1, TIDL_BatchNormLayer           ,  1,   1 ,  1 ,  0 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  1 ,    1 ,    3 ,  112 ,   96 ,    1 ,    3 ,  112 ,   96 ,
  2, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  1 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  2 ,    1 ,    3 ,  112 ,   96 ,    1 ,   32 ,   56 ,   48 ,
  3, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  2 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  3 ,    1 ,   32 ,   56 ,   48 ,    1 ,   32 ,   28 ,   24 ,
  4, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  3 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  4 ,    1 ,   32 ,   28 ,   24 ,    1 ,   64 ,   28 ,   24 ,
  5, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  4 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  5 ,    1 ,   64 ,   28 ,   24 ,    1 ,   64 ,   14 ,   12 ,
  6, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  5 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  6 ,    1 ,   64 ,   14 ,   12 ,    1 ,  128 ,   14 ,   12 ,
  7, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  6 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  7 ,    1 ,  128 ,   14 ,   12 ,    1 ,  128 ,    7 ,    6 ,
  8, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  7 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  8 ,    1 ,  128 ,    7 ,    6 ,    1 ,  256 ,    7 ,    6 ,
  9, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  8 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  9 ,    1 ,  256 ,    7 ,    6 ,    1 ,  256 ,    3 ,    3 ,
 10, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  9 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 10 ,    1 ,  256 ,    3 ,    3 ,    1 ,  512 ,    3 ,    3 ,
 11, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 10 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 11 ,    1 ,  512 ,    3 ,    3 ,    1 ,  512 ,    3 ,    3 ,
 12, TIDL_PoolingLayer             ,  1,   1 ,  1 , 11 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 12 ,    1 ,  512 ,    3 ,    3 ,    1 ,    1 ,    1 ,  512 ,
 13, TIDL_InnerProductLayer        ,  1,   1 ,  1 , 12 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 13 ,    1 ,    1 ,    1 ,  512 ,    1 ,    1 ,    1 ,  512 ,
 14, TIDL_DataLayer                ,  0,   1 , -1 , 13 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  0 ,    1 ,    1 ,    1 ,  512 ,    0 ,    0 ,    0 ,    0 ,
Layer ID    ,inBlkWidth  ,inBlkHeight ,inBlkPitch  ,outBlkWidth ,outBlkHeight,outBlkPitch ,numInChs    ,numOutChs   ,numProcInChs,numLclInChs ,numLclOutChs,numProcItrs ,numAccItrs  ,numHorBlock ,numVerBlock ,inBlkChPitch,outBlkChPitc,alignOrNot 
      2          104           36          104           48           14           48            3           32            3            1            8            1            3            1            4         3744          672            1    
      3           56           16           56           48           14           48            8            8            8            4            8            1            2            1            4          896          672            1    
      4           40           30           40           32           28           32           32           64           32            7            8            1            5            1            1         1200          896            1    
      5           40           30           40           32           28           32           16           16           16            7            8            1            3            1            1         1200          896            1    
      6           24           16           24           16           14           16           64          128           64            8            8            1            8            1            1          384          224            1    
      7           24           16           24           16           14           16           32           32           32            8            8            1            4            1            1          384          224            1    
      8           24            9           24           16            7           16          128          256          128            8            8            1           16            1            1          216          112            1    
      9           24            8           24           16            6           16           64           64           64            8            8            1            8            1            1          192           96            1    
     10           24            5           24           16            3           16          256          512          256            8            8            1           32            1            1          120           48            1    
     11           24            5           24           16            3           16          128          128          128            8            8            1           16            1            1          120           48            1    

Processing Frame Number : 0 

 Layer    1 : Out Q :      280 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.03,     0.03, Sparsity :   0.00
 Layer    2 : Out Q :    44696 , TIDL_ConvolutionLayer, PASSED  #MMACs =     6.45,     3.38, Sparsity :  47.67
 Layer    3 : Out Q :    25963 , TIDL_ConvolutionLayer, PASSED  #MMACs =     6.19,     2.12, Sparsity :  65.80
 Layer    4 : Out Q :    57240 , TIDL_ConvolutionLayer, PASSED  #MMACs =    12.39,     2.83, Sparsity :  77.15
 Layer    5 : Out Q :    62339 , TIDL_ConvolutionLayer, PASSED  #MMACs =     6.19,     2.03, Sparsity :  67.27
 Layer    6 : Out Q :    75942 , TIDL_ConvolutionLayer, PASSED  #MMACs =    12.39,     2.70, Sparsity :  78.16
 Layer    7 : Out Q :    69657 , TIDL_ConvolutionLayer, PASSED  #MMACs =     6.19,     1.59, Sparsity :  74.28
 Layer    8 : Out Q :    84570 , TIDL_ConvolutionLayer, PASSED  #MMACs =    12.39,     2.61, Sparsity :  78.94
 Layer    9 : Out Q :    83507 , TIDL_ConvolutionLayer, PASSED  #MMACs =     5.31,     1.18, Sparsity :  77.72
 Layer   10 : Out Q :    34892 , TIDL_ConvolutionLayer, PASSED  #MMACs =    10.62,     2.15, Sparsity :  79.76
 Layer   11 : Out Q :     1515 , TIDL_ConvolutionLayer, PASSED  #MMACs =     5.31,     0.84, Sparsity :  84.15
 Layer   12 : Out Q :     2066 , TIDL_PoolingLayer,     PASSED  #MMACs =     0.00,     0.00, Sparsity :   0.00
 Layer   13 : Out Q :      604 , TIDL_InnerProductLayer,     PASSED  #MMACs =     0.00,     0.00, Sparsity :   0.00
End of config list found !
Caffe Network File : .\deploy.prototxt  
Caffe Model File   : .\sparse_iter_260000.caffemodel  
TIDL Network File  : .\tidl_net_imagenet_jacintonet11v2.bin  
TIDL Model File    : .\tidl_param_imagenet_jacintonet11v2.bin  
Name of the Network : jacintonet11v2_deploy 
Num Inputs :               1 
 Num of Layer Detected :  14 
  0, TIDL_DataLayer                , data                                      0,  -1 ,  1 ,   x ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  0 ,       0 ,       0 ,       0 ,       0 ,       1 ,       3 ,     112 ,      96 ,         0 ,
  1, TIDL_BatchNormLayer           , data/bias                                 1,   1 ,  1 ,   0 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  1 ,       1 ,       3 ,     112 ,      96 ,       1 ,       3 ,     112 ,      96 ,     32256 ,
  2, TIDL_ConvolutionLayer         , conv1a                                    1,   1 ,  1 ,   1 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  2 ,       1 ,       3 ,     112 ,      96 ,       1 ,      32 ,      56 ,      48 ,   6451200 ,
  3, TIDL_ConvolutionLayer         , conv1b                                    1,   1 ,  1 ,   2 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  3 ,       1 ,      32 ,      56 ,      48 ,       1 ,      32 ,      28 ,      24 ,   6193152 ,
  4, TIDL_ConvolutionLayer         , res2a_branch2a                            1,   1 ,  1 ,   3 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  4 ,       1 ,      32 ,      28 ,      24 ,       1 ,      64 ,      28 ,      24 ,  12386304 ,
  5, TIDL_ConvolutionLayer         , res2a_branch2b                            1,   1 ,  1 ,   4 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  5 ,       1 ,      64 ,      28 ,      24 ,       1 ,      64 ,      14 ,      12 ,   6193152 ,
  6, TIDL_ConvolutionLayer         , res3a_branch2a                            1,   1 ,  1 ,   5 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  6 ,       1 ,      64 ,      14 ,      12 ,       1 ,     128 ,      14 ,      12 ,  12386304 ,
  7, TIDL_ConvolutionLayer         , res3a_branch2b                            1,   1 ,  1 ,   6 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  7 ,       1 ,     128 ,      14 ,      12 ,       1 ,     128 ,       7 ,       6 ,   6193152 ,
  8, TIDL_ConvolutionLayer         , res4a_branch2a                            1,   1 ,  1 ,   7 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  8 ,       1 ,     128 ,       7 ,       6 ,       1 ,     256 ,       7 ,       6 ,  12386304 ,
  9, TIDL_ConvolutionLayer         , res4a_branch2b                            1,   1 ,  1 ,   8 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  9 ,       1 ,     256 ,       7 ,       6 ,       1 ,     256 ,       3 ,       3 ,   6193152 ,
 10, TIDL_ConvolutionLayer         , res5a_branch2a                            1,   1 ,  1 ,   9 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 10 ,       1 ,     256 ,       3 ,       3 ,       1 ,     512 ,       3 ,       3 ,  10616832 ,
 11, TIDL_ConvolutionLayer         , res5a_branch2b                            1,   1 ,  1 ,  10 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 11 ,       1 ,     512 ,       3 ,       3 ,       1 ,     512 ,       3 ,       3 ,   5308416 ,
 12, TIDL_PoolingLayer             , pool5                                     1,   1 ,  1 ,  11 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 12 ,       1 ,     512 ,       3 ,       3 ,       1 ,       1 ,       1 ,     512 ,      4608 ,
 13, TIDL_InnerProductLayer        , fc512                                     1,   1 ,  1 ,  12 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 13 ,       1 ,       1 ,       1 ,     512 ,       1 ,       1 ,       1 ,     512 ,    262144 ,
Total Giga Macs : 0.0846

    and this is my  signed float feature_map(five pictures) which get on pc.

    Fullscreen feature_test_pc_float.txt Download 
    -2.3334 -4.81361 1.60106 -9.30873 -0.493946 -4.75249 5.27968 6.08396 27.6322 -16.4587 -20.2266 1.56878 20.2298 -1.8637 4.7909 3.0148 -6.0638 2.54976 9.82974 -3.44838 -6.18992 -8.50193 -0.683365 -3.36583 -24.4481 7.60339 8.18641 9.81735 15.3494 35.649 5.32655 2.44044 -1.59215 11.2689 25.7513 18.3126 -12.3896 -13.4268 0.10453 -15.7165 -4.47136 18.3486 -3.26482 -36.8464 -0.432096 12.8037 3.39937 -8.10574 31.3493 -6.12645 2.93857 1.79671 21.3715 -2.00693 -25.1029 -3.82327 -11.8207 -8.14864 -16.9127 -3.22039 -20.7511 2.78417 -12.2627 -16.8245 0.444898 25.0366 -1.35278 -20.3338 -6.52115 1.98465 -13.8391 -14.3194 -5.48992 -14.1982 2.60098 -16.152 4.79302 20.5582 -5.2419 1.9436 -5.51969 -6.87787 1.58516 -16.8141 30.4352 -1.57302 -4.04291 23.9071 23.1974 -22.3076 2.97779 -17.3386 -56.8036 -1.66354 8.87112 -13.834 2.80118 17.4985 -4.80526 36.8068 -9.41476 3.15715 10.2094 -0.322741 -0.35931 -8.79215 0.626971 2.5425 5.0345 27.262 3.80363 -9.91583 -6.83387 -13.4228 -2.1281 -20.2668 -6.73206 20.6714 11.4042 9.29669 -3.5501 22.3547 -2.14618 7.99318 -1.97175 -3.33421 -1.76497 -12.6968 -1.63122 -2.39401 2.15151 38.4657 -11.6015 11.119 8.5653 1.63941 -18.3128 -4.18314 11.79 -0.291191 16.4825 3.20393 9.39609 2.20761 9.09408 7.74085 1.78107 -0.933219 15.5265 -1.30823 29.405 9.81641 6.36743 9.4528 -17.0515 20.42 -7.15946 -7.65383 4.63895 14.7602 -14.0502 -4.0508 -3.60522 0.491613 10.6244 -5.8734 -12.9944 4.61909 16.2477 -3.13905 28.1426 -14.9581 13.16 -2.07341 -11.7324 24.8041 -12.9533 29.2236 -6.46296 -5.34815 -13.2047 53.8642 21.269 13.2226 6.55779 -3.47197 -4.26313 -26.351 11.1354 -14.12 -8.80242 -9.54684 -3.89718 24.5648 50.116 -22.2132 -6.82456 6.02304 -17.4577 26.1397 -1.80863 1.98271 -3.38223 -6.82662 5.27251 9.80777 2.81638 -16.7506 -6.85512 -28.7844 9.48499 -2.59856 2.48267 -2.59536 0.582695 -1.10742 -38.071 -5.37845 10.2263 6.45528 3.16281 11.6214 14.1071 1.01091 39.7115 -12.8078 14.0483 33.8953 6.37947 -18.4839 -7.396 11.9408 -0.718152 13.8886 1.0739 -2.41918 8.28947 1.30642 -0.790641 11.3302 -0.0799343 -1.6119 -6.389 1.19512 -9.4305 -5.60394 5.98932 13.7375 -1.55099 -2.45518 15.3358 -4.231 -10.1379 -3.48792 -11.2466 -2.04221 -12.5989 -6.21459 -6.46298 4.73988 8.6152 -14.1917 -9.7783 2.3839 6.7981 2.25158 -6.59558 -0.689742 -26.4607 15.7008 -5.70325 11.8557 -12.88 14.8754 -0.189722 -21.9042 5.63091 -9.23771 3.95701 0.807939 6.87044 4.8605 -10.7141 12.418 -1.86311 -0.703352 -4.38501 9.99384 -6.62351 -17.9549 1.29476 0.810474 -18.0173 11.1637 12.1175 5.89883 -3.65851 -2.26226 7.06022 2.95869 5.65398 24.076 32.1941 1.22461 9.47546 -0.40749 -3.72973 -3.11252 -12.0489 34.2253 -0.878202 23.5413 15.0571 -5.33715 2.85414 -19.7179 -6.43657 -11.8562 -2.10077 0.0604749 34.0017 -2.42134 3.24926 -2.65501 6.42227 -8.97397 15.6429 22.4047 8.75416 29.2057 -6.64846 19.4148 -18.7468 3.63491 0.828373 10.3141 -1.52132 -0.910444 -20.9111 3.52483 -0.568277 -1.60871 -3.69154 -17.7355 -3.22047 -2.17983 -2.22577 3.31486 1.53489 16.9885 16.8004 -1.0627 -10.6325 6.41251 -5.3592 16.3713 -14.8237 27.6976 7.55428 1.91238 11.4223 2.4167 -6.44436 26.0733 0.667094 8.44119 -1.33141 3.54791 7.72181 -40.7186 -1.62548 -17.5429 6.93805 29.1319 0.113849 -1.0492 21.0603 13.8555 5.46937 -10.0604 -24.2068 -1.90445 4.68412 -27.8554 11.2131 4.4025 -0.819602 -11.861 17.7316 -11.0856 -36.8061 2.12829 11.2128 5.248 15.206 14.4841 -5.60177 1.62917 10.6194 12.4003 2.05164 -7.33833 14.7466 -2.51764 -21.5782 -1.38297 3.88603 3.46104 5.9525 5.01948 7.22153 -1.06942 14.6674 -16.6386 -16.1606 29.0062 16.7019 6.70052 43.9636 -11.743 14.0494 -8.64186 -20.6555 -33.1825 9.25403 -21.0165 9.951 1.32858 -8.51196 -0.547932 -0.870848 15.479 -2.37392 -2.66762 -14.7011 35.5043 -4.79761 27.9751 -10.8951 -26.2556 28.8044 -3.23609 7.07136 -8.35432 -13.0011 -7.0073 19.0487 21.1109 -17.7706 -8.73993 -19.0763 -9.67319 8.9338 16.355 8.2177 -2.17686 31.1172 -12.9435 6.86955 -0.240397 8.89036 -0.918987 10.1819 2.47727 29.0431 17.0378 11.1685 -28.2493 21.5075 4.5157 -33.0988 -16.6525 -19.5829 0.421631 -3.02243 27.2616 5.66694 23.6034 14.31 -10.5839 4.54701 8.10712 -0.130011 11.0431 7.58234 -7.60033 7.84467 5.7101 2.24755 0.282316 -13.809 -8.39266 0.942529 -4.82941 5.60393 30.2369 14.9752 2.66381 9.03682 4.78893 -19.4477 -7.75029 4.19981 11.1455 -13.0899 2.48952 -0.357618 46.71 26.9566 7.27536 11.9147 -6.9309 
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     this is signed char feature_map (the same five pictures) which get on AM5728.

    Fullscreen feature_test_board_char.txt Download 
    0 226 1 237 0 252 5 3 24 252 225 0 4 253 4 19 1 10 253 227 243 246 254 231 3 0 5 19 9 33 5 254 6 252 18 17 239 253 245 240 248 5 1 241 18 11 2 254 19 5 19 6 19 251 246 255 8 245 219 245 252 7 6 244 243 250 1 11 251 235 3 3 245 221 6 245 4 41 252 247 253 249 6 228 23 232 0 251 27 224 254 229 209 237 10 234 252 26 24 36 235 16 28 19 244 5 255 9 2 55 12 254 255 3 248 229 248 10 4 13 252 18 2 2 248 1 249 242 245 254 6 58 36 13 11 8 225 2 6 0 16 1 25 4 13 254 10 4 41 0 41 240 251 0 241 246 250 237 253 13 9 252 251 1 7 241 3 7 4 246 8 250 246 255 4 28 241 17 244 246 217 34 32 8 4 6 254 246 4 13 251 245 255 18 33 233 0 0 30 37 255 8 250 3 250 254 255 246 248 220 8 250 255 250 0 2 225 1 3 5 254 8 20 245 32 246 29 19 8 13 239 7 255 4 249 254 9 254 8 6 0 3 251 0 246 248 7 5 7 253 12 9 238 253 235 3 247 239 252 9 7 238 11 245 247 0 252 2 238 10 3 19 5 33 6 248 252 242 2 248 1 1 253 9 253 253 253 246 4 249 255 254 241 4 7 14 6 3 248 5 10 35 28 255 17 2 6 242 234 11 0 30 24 254 2 240 253 246 253 1 23 0 0 13 10 247 254 13 23 15 238 33 227 5 6 3 2 7 238 11 252 251 250 249 247 12 255 252 230 2 12 17 243 255 254 17 239 9 3 3 253 7 249 19 253 2 253 11 12 235 0 240 255 18 2 5 7 22 8 233 3 255 255 224 21 250 253 234 6 7 245 250 9 9 4 254 251 22 231 25 251 243 13 232 230 250 0 2 251 8 237 5 9 243 247 22 36 254 25 1 252 246 239 226 6 248 11 254 253 14 6 255 19 5 2 15 254 19 251 233 14 0 6 247 243 0 25 39 246 252 231 231 3 12 11 254 26 247 0 1 253 6 2 251 11 4 253 215 251 11 220 242 254 253 254 43 0 253 5 222 7 6 0 3 250 255 243 245 6 241 0 245 8 253 18 32 16 247 3 6 209 243 255 242 3 3 253 55 19 5 13 252 
247 238 243 12 241 251 27 254 8 10 7 2 1 246 0 246 2 4 11 240 248 251 0 223 14 255 12 5 18 251 10 255 24 218 254 11 250 2 236 243 236 26 254 3 247 1 3 3 220 1 9 253 3 248 22 244 23 39 247 5 247 27 249 6 247 233 8 251 248 245 4 7 253 240 0 254 0 14 253 252 2 225 17 242 16 248 2 219 249 224 253 231 254 8 2 241 244 2 1 237 251 250 2 12 242 12 250 7 11 36 16 251 0 8 254 2 1 250 1 0 251 19 2 4 251 244 253 25 248 3 252 36 227 8 241 4 227 254 238 15 242 240 9 250 246 252 239 3 4 5 248 253 253 232 232 211 248 3 3 25 253 26 248 254 11 253 9 1 253 0 228 25 237 255 18 23 246 5 233 2 196 5 221 11 241 248 11 7 8 249 38 5 7 0 41 253 21 12 51 55 1 244 245 5 5 246 255 8 252 247 14 240 3 4 0 242 250 15 250 4 253 250 251 249 255 212 254 0 254 247 254 1 4 8 243 4 23 0 232 249 8 243 7 0 249 8 19 11 255 8 3 2 17 3 248 250 255 4 234 248 1 236 229 16 252 10 236 2 227 248 15 20 6 244 5 9 29 13 248 254 2 45 241 28 253 10 255 244 235 250 5 22 31 9 3 231 245 0 3 1 14 16 30 247 248 2 19 247 6 5 0 10 23 250 3 222 1 0 251 2 237 8 3 0 2 10 11 18 11 26 0 248 3 239 249 219 255 7 4 7 18 245 0 8 244 5 0 245 246 5 11 2 230 0 254 23 248 225 247 0 11 255 1 254 246 253 251 11 254 10 40 245 244 11 14 254 235 32 7 216 4 5 251 251 1 244 245 8 248 39 225 251 252 14 10 237 1 21 220 7 241 6 10 240 252 249 252 254 247 15 7 253 15 10 247 253 9 2 12 3 6 251 240 252 28 8 250 2 234 12 5 11 248 9 29 241 253 237 243 254 201 249 7 239 245 1 5 10 16 245 8 246 8 248 31 251 31 242 243 0 235 247 237 250 35 0 237 248 241 254 14 30 1 20 7 254 230 228 233 229 4 2 253 231 250 9 3 248 5 239 22 10 2 20 27 8 249 240 1 251 29 10 249 251 31 24 249 243 238 7 255 252 
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    even I divide the scale factor 2.3593  , the result is not corresponding, so please illustrate how to convert the feature_map by  giving an example.

    thanks

    best regards

  • 0 in reply to Happy_Bobo
    TI__Mastermind 22145 points
    Can you check the similar matching for input and first layer(Trace 0 and trace 1) to make sure your input and first convolution is matching.
  • 0 in reply to kumar.desappan
    Intellectual 325 points
    thanks for your help, I have solved my question.
    I use the signed char* to point the output buffer, and the result is reasonable.
  • 0 in reply to Happy_Bobo
    TI__Mastermind 22145 points
    Thanks for confirmimg. Closing the thread.