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

Happy_Bobo

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

over 6 years ago

0 Praveen Eppa1 over 6 years ago

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 Happy_Bobo over 6 years ago 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 Praveen Eppa1 over 6 years ago 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 Happy_Bobo over 6 years ago 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 ??

convert_log.txt

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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 ,
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX

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.

feature_test_pc_float.txt

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5
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-2.23069 -3.90371 1.67302 -9.48043 -0 -4.46138 4.46138 6.13439 27.8836 -16.7302 -19.5185 1.67302 20.0762 -1.67302 5.57672 3.34603 -5.57672 3.34603 10.0381 -3.34603 -6.13439 -8.36508 -0.557672 -1.67302 -23.9799 7.80741 8.36508 10.0381 16.1725 34.5757 5.57672 2.78836 -0.557672 11.7111 26.2106 18.4032 -12.2688 -12.8265 0.557672 -15.6148 -4.46138 17.2878 -2.78836 -37.364 0.557672 12.8265 3.90371 -7.80741 30.672 -6.13439 3.34603 2.23069 21.1915 -2.23069 -25.0953 -3.90371 -11.7111 -7.80741 -16.7302 -2.78836 -20.6339 2.23069 -12.2688 -16.1725 0.557672 23.9799 -1.11534 -19.5185 -6.13439 2.23069 -13.9418 -14.4995 -4.46138 -14.4995 2.78836 -15.6148 4.46138 20.6339 -5.01905 2.23069 -5.01905 -6.13439 1.67302 -17.8455 30.1143 -1.67302 -3.90371 23.4222 23.4222 -21.7492 3.34603 -17.8455 -56.8826 -1.11534 8.92276 -13.3841 3.34603 17.2878 -4.46138 36.8064 -9.48043 3.34603 11.1534 -0 -0.557672 -8.36508 0.557672 3.34603 5.01905 27.3259 3.90371 -9.48043 -6.69207 -13.3841 -1.67302 -19.5185 -6.69207 20.0762 11.7111 10.0381 -3.34603 22.8646 -1.67302 8.36508 -2.23069 -3.34603 -1.67302 -13.3841 -1.11534 -2.23069 2.23069 38.4794 -11.7111 11.1534 8.92276 2.23069 -17.2878 -3.34603 11.1534 0 16.7302 3.34603 10.0381 2.78836 8.92276 7.80741 1.67302 -0 15.6148 -1.11534 28.999 10.5958 6.69207 9.48043 -17.2878 20.0762 -6.13439 -8.36508 5.01905 15.0572 -13.3841 -3.34603 -2.78836 0.557672 10.5958 -5.57672 -12.8265 5.01905 16.7302 -3.34603 27.8836 -13.9418 13.3841 -1.67302 -11.1534 25.6529 -12.8265 28.4413 -6.13439 -5.57672 -13.3841 55.2096 20.6339 13.3841 7.24974 -2.78836 -3.34603 -25.6529 11.1534 -13.9418 -8.36508 -10.0381 -3.34603 24.5376 49.6328 -21.7492 -6.13439 6.69207 -16.7302 26.2106 -1.11534 2.78836 -3.34603 -6.13439 5.57672 9.48043 2.78836 -16.1725 -6.13439 -27.8836 9.48043 -1.67302 2.78836 -3.34603 1.11534 -1.11534 -37.9217 -5.01905 10.5958 6.69207 3.34603 11.7111 14.4995 1.67302 40.1524 -12.8265 14.4995 33.4603 6.69207 -18.4032 -6.13439 11.7111 -0.557672 14.4995 1.67302 -2.23069 8.92276 2.23069 -0.557672 12.2688 0 -1.11534 -5.57672 1.67302 -9.48043 -5.01905 6.13439 13.9418 -1.11534 -2.23069 15.6148 -3.90371 -9.48043 -3.34603 -11.1534 -1.67302 -12.2688 -6.13439 -6.13439 4.46138 8.92276 -14.4995 -9.48043 2.23069 6.69207 2.78836 -6.13439 -0 -26.7683 15.6148 -5.01905 11.7111 -12.8265 14.4995 0.557672 -21.7492 6.13439 -8.92276 4.46138 0.557672 7.24974 6.69207 -11.7111 14.4995 -1.67302 -0.557672 -3.90371 10.0381 -6.69207 -17.8455 1.11534 1.11534 -18.4032 11.1534 12.8265 5.01905 -3.34603 -2.23069 7.24974 3.34603 6.69207 25.0953 32.345 1.67302 9.48043 -0 -3.34603 -2.78836 -11.1534 34.018 -0.557672 23.9799 15.6148 -5.01905 3.34603 -18.9609 -6.13439 -12.2688 -2.23069 0.557672 34.018 -2.23069 3.34603 -2.23069 6.69207 -8.92276 16.1725 22.3069 8.92276 29.5566 -5.57672 19.5185 -18.4032 3.90371 1.11534 10.5958 -1.11534 -0.557672 -20.6339 3.90371 -0.557672 -0.557672 -3.34603 -17.2878 -2.78836 -1.67302 -1.67302 4.46138 0.557672 16.7302 17.2878 -1.67302 -10.0381 6.69207 -4.46138 16.1725 -15.0572 27.8836 7.24974 2.23069 11.7111 2.23069 -6.13439 26.2106 1.11534 8.92276 -1.11534 3.90371 8.36508 -40.1524 -1.11534 -17.8455 7.24974 30.1143 0.557672 -1.11534 21.1915 15.0572 5.57672 -10.5958 -24.5376 -1.67302 4.46138 -26.7683 11.7111 5.01905 0 -11.7111 17.8455 -11.1534 -36.2487 2.23069 11.7111 5.01905 15.6148 13.9418 -5.01905 2.23069 11.1534 12.2688 2.23069 -7.24974 15.6148 -2.23069 -20.6339 -1.11534 4.46138 3.34603 6.13439 5.57672 7.24974 -1.11534 15.6148 -16.7302 -16.7302 28.999 16.1725 7.24974 45.1715 -11.7111 13.9418 -8.92276 -20.0762 -32.9027 9.48043 -20.6339 10.0381 1.67302 -7.80741 -0 -0.557672 16.7302 -2.23069 -2.78836 -13.9418 35.691 -4.46138 27.8836 -10.5958 -25.6529 28.4413 -2.78836 6.69207 -8.36508 -12.8265 -6.13439 19.5185 21.7492 -17.2878 -9.48043 -18.4032 -8.92276 9.48043 16.1725 9.48043 -1.67302 31.7873 -12.8265 6.69207 0 8.92276 -0.557672 11.1534 2.78836 27.8836 16.7302 11.1534 -27.3259 20.6339 5.01905 -32.345 -16.7302 -18.9609 0 -2.78836 26.7683 6.13439 23.4222 14.4995 -10.5958 5.01905 8.36508 0 11.7111 7.24974 -7.24974 8.36508 6.69207 2.23069 0.557672 -13.9418 -7.24974 1.11534 -5.01905 6.13439 30.1143 14.4995 2.78836 9.48043 5.01905 -18.9609 -7.80741 4.46138 10.5958 -12.2688 2.23069 -0 45.7291 26.7683 7.80741 11.7111 -6.13439

this is signed char feature_map (the same five pictures) which get on AM5728.

feature_test_board_char.txt

Fullscreen

1
2
3
4
5
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 
1 234 0 241 0 254 4 3 15 254 233 0 4 253 3 13 0 4 254 236 245 247 255 241 2 0 5 13 7 25 4 255 3 253 12 11 244 253 248 246 250 4 1 246 12 6 1 254 13 4 13 4 13 253 250 254 4 251 230 248 252 4 2 248 248 252 1 10 253 241 2 2 249 232 4 249 3 29 253 250 254 251 4 237 17 240 0 252 20 233 254 238 222 243 8 241 254 19 18 24 242 8 20 12 248 4 255 7 3 40 8 255 255 3 249 237 250 6 3 8 253 14 1 2 250 1 251 246 249 255 4 42 25 8 8 6 234 2 5 253 12 0 18 3 10 255 8 3 27 1 29 244 252 0 244 249 249 243 254 9 5 254 253 1 3 246 2 5 3 249 6 251 250 255 3 20 246 12 248 249 230 25 25 4 1 5 254 250 3 8 253 249 254 12 23 240 1 0 18 25 255 6 251 2 251 254 0 250 249 231 6 252 0 252 0 1 234 1 2 4 254 5 14 249 22 248 21 15 6 7 246 6 255 3 251 254 8 254 5 3 255 2 252 0 249 250 3 5 5 253 7 5 243 254 242 2 249 243 254 7 5 243 8 249 249 0 252 2 243 7 0 15 3 25 5 250 252 245 1 250 0 1 255 5 254 254 253 249 3 253 255 0 245 3 5 12 5 1 251 3 7 25 20 255 12 1 5 246 242 7 255 21 17 255 3 245 253 250 253 1 14 0 1 10 7 251 255 10 16 10 243 23 237 4 3 1 1 5 241 9 253 254 252 251 250 9 255 254 237 2 9 11 249 255 254 11 243 8 3 2 255 6 251 14 255 1 253 7 5 241 254 244 255 11 2 4 5 13 6 241 2 255 0 232 15 252 0 241 5 4 248 253 7 7 4 254 251 16 236 17 253 247 10 238 237 252 0 2 252 5 244 4 6 248 250 17 24 255 18 1 253 250 241 234 5 249 10 255 254 8 4 1 13 2 0 10 254 13 251 241 12 0 4 249 247 0 18 26 249 254 238 237 3 10 7 255 17 249 0 1 254 3 1 253 7 3 254 229 254 8 231 246 255 255 254 30 1 253 4 233 6 4 0 2 252 255 246 248 4 246 1 249 6 254 13 23 13 250 3 4 223 246 0 247 1 2 255 39 12 4 9 253 
0 254 253 21 251 239 247 252 2 13 248 3 4 248 252 1 250 7 41 246 0 244 253 221 7 254 239 17 251 243 254 255 20 3 10 5 242 5 251 248 0 248 22 241 14 1 254 255 229 253 7 2 15 242 252 241 21 255 24 253 2 16 254 11 251 244 4 21 7 254 8 10 241 250 2 252 9 8 3 1 255 245 12 246 11 239 7 232 250 215 255 228 15 244 10 2 251 247 250 253 247 2 19 12 240 11 255 2 254 2 9 1 250 252 251 250 254 4 4 1 249 0 2 3 237 235 0 240 248 0 3 8 8 5 245 5 242 255 34 28 247 254 4 2 243 5 8 252 14 7 8 238 253 248 241 243 6 2 252 34 9 11 246 3 9 0 5 5 14 232 244 5 255 0 249 242 1 2 252 254 226 255 1 237 26 248 252 255 3 7 25 252 6 30 1 252 254 5 37 245 11 0 5 8 9 250 252 225 254 231 0 249 251 231 255 1 247 16 1 1 236 254 6 12 17 11 22 249 5 24 0 247 7 5 254 1 11 250 255 254 251 2 4 0 246 253 8 27 252 17 4 8 247 4 221 253 11 240 255 255 235 2 254 7 253 9 18 254 236 24 45 20 254 245 247 15 25 234 0 246 9 29 237 11 0 14 10 9 13 247 253 254 254 15 6 244 253 4 252 13 17 0 21 255 253 4 14 239 16 21 0 19 13 4 2 5 1 238 4 2 10 5 255 14 1 241 8 1 13 20 13 234 7 253 13 1 1 4 17 5 253 254 2 3 8 27 4 3 248 6 8 8 225 13 8 234 3 244 0 0 237 220 254 11 0 252 241 2 0 32 25 248 5 8 26 238 5 3 5 0 4 13 254 8 11 248 239 0 252 30 10 250 245 235 255 243 14 17 250 2 251 253 5 255 239 249 3 254 242 6 2 8 6 7 242 0 27 250 5 254 240 239 233 4 5 245 254 1 250 7 3 249 250 253 24 235 6 237 243 247 0 1 10 6 250 239 246 14 4 5 254 2 254 255 252 250 255 253 2 0 239 4 254 0 7 6 226 237 247 2 29 10 13 249 10 0 253 245 246 244 255 246 0 240 236 6 11 252 9 244 14 9 250 3 25 250 8 240 247 1 0 13 249 8 248 15 254 250 252 247 251 249 
1 215 1 226 255 250 9 6 30 253 211 0 7 250 4 24 0 7 252 217 235 240 253 224 3 255 10 26 14 48 9 254 8 250 25 20 232 251 239 235 246 8 1 235 24 13 3 252 25 8 25 9 25 250 243 253 10 246 206 240 248 8 4 240 241 248 2 19 249 227 2 3 240 208 9 241 5 57 251 244 251 249 8 219 34 226 0 250 38 212 253 222 187 231 14 224 252 36 35 46 228 16 40 25 242 7 255 13 5 82 15 252 0 5 243 215 245 14 6 17 250 27 2 4 243 2 246 237 245 253 8 81 48 16 17 11 213 4 10 250 26 1 36 5 20 253 15 5 53 2 60 234 248 0 232 243 242 233 251 18 10 251 250 2 7 235 5 9 7 244 11 247 242 255 6 38 236 25 241 242 204 50 49 8 3 9 254 245 6 17 248 242 254 22 48 226 3 1 36 51 254 12 247 3 246 255 1 243 243 207 11 248 255 248 1 3 213 2 4 9 253 9 27 241 43 241 40 30 12 14 236 11 255 6 247 253 14 253 13 5 255 3 248 0 243 243 8 10 9 250 15 11 231 252 230 2 242 231 252 17 11 229 14 243 241 255 248 3 230 15 0 30 6 52 9 245 249 235 2 242 0 3 0 12 252 251 251 242 5 249 254 255 234 6 9 22 9 3 248 5 13 47 40 255 24 3 7 236 229 15 255 40 33 253 4 234 249 246 251 2 31 255 255 18 14 245 255 21 32 21 231 46 217 7 7 1 3 8 227 16 250 252 248 247 244 17 255 253 220 4 18 22 241 253 252 22 232 15 5 5 252 9 247 27 253 3 250 13 10 227 254 234 255 22 3 9 9 24 12 225 4 254 1 207 29 249 254 226 11 7 239 251 12 12 7 254 248 30 218 35 252 238 19 222 218 248 0 4 248 9 233 8 11 242 244 34 47 254 35 3 250 245 227 211 11 244 19 254 251 15 9 3 27 5 255 20 253 26 245 226 24 0 7 239 239 0 35 49 242 253 221 223 5 20 14 254 34 242 1 1 254 7 2 250 15 7 253 203 255 16 207 237 0 254 250 60 3 250 8 212 12 9 255 3 248 255 235 240 8 236 3 240 11 255 26 47 24 245 5 9 191 236 0 237 1 4 253 77 25 7 18 249 
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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 
1 234 0 241 0 254 4 3 15 254 233 0 4 253 3 13 0 4 254 236 245 247 255 241 2 0 5 13 7 25 4 255 3 253 12 11 244 253 248 246 250 4 1 246 12 6 1 254 13 4 13 4 13 253 250 254 4 251 230 248 252 4 2 248 248 252 1 10 253 241 2 2 249 232 4 249 3 29 253 250 254 251 4 237 17 240 0 252 20 233 254 238 222 243 8 241 254 19 18 24 242 8 20 12 248 4 255 7 3 40 8 255 255 3 249 237 250 6 3 8 253 14 1 2 250 1 251 246 249 255 4 42 25 8 8 6 234 2 5 253 12 0 18 3 10 255 8 3 27 1 29 244 252 0 244 249 249 243 254 9 5 254 253 1 3 246 2 5 3 249 6 251 250 255 3 20 246 12 248 249 230 25 25 4 1 5 254 250 3 8 253 249 254 12 23 240 1 0 18 25 255 6 251 2 251 254 0 250 249 231 6 252 0 252 0 1 234 1 2 4 254 5 14 249 22 248 21 15 6 7 246 6 255 3 251 254 8 254 5 3 255 2 252 0 249 250 3 5 5 253 7 5 243 254 242 2 249 243 254 7 5 243 8 249 249 0 252 2 243 7 0 15 3 25 5 250 252 245 1 250 0 1 255 5 254 254 253 249 3 253 255 0 245 3 5 12 5 1 251 3 7 25 20 255 12 1 5 246 242 7 255 21 17 255 3 245 253 250 253 1 14 0 1 10 7 251 255 10 16 10 243 23 237 4 3 1 1 5 241 9 253 254 252 251 250 9 255 254 237 2 9 11 249 255 254 11 243 8 3 2 255 6 251 14 255 1 253 7 5 241 254 244 255 11 2 4 5 13 6 241 2 255 0 232 15 252 0 241 5 4 248 253 7 7 4 254 251 16 236 17 253 247 10 238 237 252 0 2 252 5 244 4 6 248 250 17 24 255 18 1 253 250 241 234 5 249 10 255 254 8 4 1 13 2 0 10 254 13 251 241 12 0 4 249 247 0 18 26 249 254 238 237 3 10 7 255 17 249 0 1 254 3 1 253 7 3 254 229 254 8 231 246 255 255 254 30 1 253 4 233 6 4 0 2 252 255 246 248 4 246 1 249 6 254 13 23 13 250 3 4 223 246 0 247 1 2 255 39 12 4 9 253 
0 254 253 21 251 239 247 252 2 13 248 3 4 248 252 1 250 7 41 246 0 244 253 221 7 254 239 17 251 243 254 255 20 3 10 5 242 5 251 248 0 248 22 241 14 1 254 255 229 253 7 2 15 242 252 241 21 255 24 253 2 16 254 11 251 244 4 21 7 254 8 10 241 250 2 252 9 8 3 1 255 245 12 246 11 239 7 232 250 215 255 228 15 244 10 2 251 247 250 253 247 2 19 12 240 11 255 2 254 2 9 1 250 252 251 250 254 4 4 1 249 0 2 3 237 235 0 240 248 0 3 8 8 5 245 5 242 255 34 28 247 254 4 2 243 5 8 252 14 7 8 238 253 248 241 243 6 2 252 34 9 11 246 3 9 0 5 5 14 232 244 5 255 0 249 242 1 2 252 254 226 255 1 237 26 248 252 255 3 7 25 252 6 30 1 252 254 5 37 245 11 0 5 8 9 250 252 225 254 231 0 249 251 231 255 1 247 16 1 1 236 254 6 12 17 11 22 249 5 24 0 247 7 5 254 1 11 250 255 254 251 2 4 0 246 253 8 27 252 17 4 8 247 4 221 253 11 240 255 255 235 2 254 7 253 9 18 254 236 24 45 20 254 245 247 15 25 234 0 246 9 29 237 11 0 14 10 9 13 247 253 254 254 15 6 244 253 4 252 13 17 0 21 255 253 4 14 239 16 21 0 19 13 4 2 5 1 238 4 2 10 5 255 14 1 241 8 1 13 20 13 234 7 253 13 1 1 4 17 5 253 254 2 3 8 27 4 3 248 6 8 8 225 13 8 234 3 244 0 0 237 220 254 11 0 252 241 2 0 32 25 248 5 8 26 238 5 3 5 0 4 13 254 8 11 248 239 0 252 30 10 250 245 235 255 243 14 17 250 2 251 253 5 255 239 249 3 254 242 6 2 8 6 7 242 0 27 250 5 254 240 239 233 4 5 245 254 1 250 7 3 249 250 253 24 235 6 237 243 247 0 1 10 6 250 239 246 14 4 5 254 2 254 255 252 250 255 253 2 0 239 4 254 0 7 6 226 237 247 2 29 10 13 249 10 0 253 245 246 244 255 246 0 240 236 6 11 252 9 244 14 9 250 3 25 250 8 240 247 1 0 13 249 8 248 15 254 250 252 247 251 249 
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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