data2/json/DLP/1030393.json

{
  "ticketNumber" : "1030393",
  "reporterName" : "lluo",
  "rankPoints" : "340",
  "resolutionStatus" : "",
  "ticketName" : "TDA2PXEVM: Is Conv with no relue supported In TDA2?",
  "rankName" : "Intellectual",
  "replies" : "",
  "views" : "",
  "queryText" : "Part Number: TDA2PXEVM Other Parts Discussed in Thread: TDA2 Hi I get some accuracy problems with caffemodel importing. Here is my steps: a) Use tidl_model_import.out.exe convert regnet.prototxt/caffemodel to tidl_param/bin b) Compare each layer's output : onnx vs trace_dump_idx_wxh.y c) I find the first conv layer(with bn & relu) matched well(error < 1%) d) But conv_layer with no relu(which are inputs of eltwise_layer) cannot match original model layer's outputs I put a snapshot below : conv layers in green rect match, but conv layer without relu in red circle not match. regnet_import.zip So, is relu strictedly demanded to place after conv layer? Or this is just bugs in import tool? I upload my model and tools for analysis.",
  "imageList" : [ "Data/input/1030393/png" ],
  "partNumber" : "NA",
  "allResponseList" : [ {
    "contentId" : "",
    "userName" : "Praveen Eppa1",
    "rankPoints" : "17580",
    "rankName" : "TI__Genius",
    "date" : "",
    "userId" : "/members/6019814",
    "content" : "Hi, Can you share the import output log to check the issue ? Thanks, Praveen",
    "imageList" : null
  }, {
    "contentId" : "",
    "userName" : "lluo",
    "rankPoints" : "340",
    "rankName" : "Intellectual",
    "date" : "",
    "userId" : "/members/5927174",
    "content" : "Hi Praveen Thanks for your replay! I uploaded LOG.txt. Geroge. 6675.LOG.txt  .\\tidl_model_import.out.exe .\\ONNX_Reg200M_CIFAR\\tidl_import.txt\r\nCaffe Network File : ONNX_Reg200M_CIFAR\\trained\\regnetx200mf_cifar_Relu96.prototxt\r\nCaffe Model File   : ONNX_Reg200M_CIFAR\\trained\\regnetx200mf_cifar_Relu96.caffemodel\r\nTIDL Network File  : ONNX_Reg200M_CIFAR\\model\\tidl_net_reg200cifar_relu96.bin\r\nTIDL Model File    : ONNX_Reg200M_CIFAR\\model\\tidl_param_reg200cifar_relu96.bin\r\nName of the Network : REG200MCIFAR-ONNX\r\nNum Inputs :               1\r\n Num of Layer Detected :  71\r\n  0, TIDL_DataLayer                , data                                      0,  -1 ,  1 ,   x ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  0 ,       0 ,       0 ,       0 ,       0 ,       1 ,       3 ,      32 ,      32 ,         0 ,\r\n  1, TIDL_ConvolutionLayer         , Conv_0                                    1,   1 ,  1 ,   0 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  1 ,       1 ,       3 ,      32 ,      32 ,       1 ,      32 ,      32 ,      32 ,    884736 ,\r\n  2, TIDL_ConvolutionLayer         , Conv_2                                    1,   1 ,  1 ,   1 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  2 ,       1 ,      32 ,      32 ,      32 ,       1 ,      24 ,      32 ,      32 ,    786432 ,\r\n  3, TIDL_ConvolutionLayer         , Conv_4                                    1,   1 ,  1 ,   2 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  3 ,       1 ,      24 ,      32 ,      32 ,       1 ,      24 ,      16 ,      16 ,    442368 ,\r\n  4, TIDL_ConvolutionLayer         , Conv_6                                    1,   1 ,  1 ,   3 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  4 ,       1 ,      24 ,      16 ,      16 ,       1 ,      24 ,      16 ,      16 ,    147456 ,\r\n  5, TIDL_ConvolutionLayer         , Conv_7                                    1,   1 ,  1 ,   1 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  5 ,       1 ,      32 ,      32 ,      32 ,       1 ,      24 ,      16 ,      16 ,    196608 ,\r\n  6, TIDL_EltWiseLayer             , Add_8                                     1,   2 ,  1 ,   4 ,  5 ,  x ,  x ,  x ,  x ,  x ,  x ,  6 ,       1 ,      24 ,      16 ,      16 ,       1 ,      24 ,      16 ,      16 ,      6144 ,\r\n  7, TIDL_BatchNormLayer           , Relu_9                                    1,   1 ,  1 ,   6 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  7 ,       1 ,      24 ,      16 ,      16 ,       1 ,      24 ,      16 ,      16 ,      6144 ,\r\n  8, TIDL_ConvolutionLayer         , Conv_10                                   1,   1 ,  1 ,   7 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  8 ,       1 ,      24 ,      16 ,      16 ,       1 ,      56 ,      16 ,      16 ,    344064 ,\r\n  9, TIDL_ConvolutionLayer         , Conv_12                                   1,   1 ,  1 ,   8 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  9 ,       1 ,      56 ,      16 ,      16 ,       1 ,      56 ,      16 ,      16 ,   1032192 ,\r\n 10, TIDL_ConvolutionLayer         , Conv_14                                   1,   1 ,  1 ,   9 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 10 ,       1 ,      56 ,      16 ,      16 ,       1 ,      56 ,      16 ,      16 ,    802816 ,\r\n 11, TIDL_ConvolutionLayer         , Conv_15                                   1,   1 ,  1 ,   7 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 11 ,       1 ,      24 ,      16 ,      16 ,       1 ,      56 ,      16 ,      16 ,    344064 ,\r\n 12, TIDL_EltWiseLayer             , Add_16                                    1,   2 ,  1 ,  10 , 11 ,  x ,  x ,  x ,  x ,  x ,  x , 12 ,       1 ,      56 ,      16 ,      16 ,       1 ,      56 ,      16 ,      16 ,     14336 ,\r\n 13, TIDL_BatchNormLayer           , Relu_17                                   1,   1 ,  1 ,  12 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 13 ,       1 ,      56 ,      16 ,      16 ,       1 ,      56 ,      16 ,      16 ,     14336 ,\r\n 14, TIDL_ConvolutionLayer         , Conv_18                                   1,   1 ,  1 ,  13 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 14 ,       1 ,      56 ,      16 ,      16 ,       1 ,     152 ,      16 ,      16 ,   2179072 ,\r\n 15, TIDL_ConvolutionLayer         , Conv_20                                   1,   1 ,  1 ,  14 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 15 ,       1 ,     152 ,      16 ,      16 ,       1 ,     152 ,       8 ,       8 ,    700416 ,\r\n 16, TIDL_ConvolutionLayer         , Conv_22                                   1,   1 ,  1 ,  15 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 16 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,   1478656 ,\r\n 17, TIDL_ConvolutionLayer         , Conv_23                                   1,   1 ,  1 ,  13 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 17 ,       1 ,      56 ,      16 ,      16 ,       1 ,     152 ,       8 ,       8 ,    544768 ,\r\n 18, TIDL_EltWiseLayer             , Add_24                                    1,   2 ,  1 ,  16 , 17 ,  x ,  x ,  x ,  x ,  x ,  x , 18 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,      9728 ,\r\n 19, TIDL_BatchNormLayer           , Relu_25                                   1,   1 ,  1 ,  18 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 19 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,      9728 ,\r\n 20, TIDL_ConvolutionLayer         , Conv_26                                   1,   1 ,  1 ,  19 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 20 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,   1478656 ,\r\n 21, TIDL_ConvolutionLayer         , Conv_28                                   1,   1 ,  1 ,  20 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 21 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,    700416 ,\r\n 22, TIDL_ConvolutionLayer         , Conv_30                                   1,   1 ,  1 ,  21 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 22 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,   1478656 ,\r\n 23, TIDL_EltWiseLayer             , Add_31                                    1,   2 ,  1 ,  22 , 19 ,  x ,  x ,  x ,  x ,  x ,  x , 23 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,      9728 ,\r\n 24, TIDL_BatchNormLayer           , Relu_32                                   1,   1 ,  1 ,  23 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 24 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,      9728 ,\r\n 25, TIDL_ConvolutionLayer         , Conv_33                                   1,   1 ,  1 ,  24 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 25 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,   1478656 ,\r\n 26, TIDL_ConvolutionLayer         , Conv_35                                   1,   1 ,  1 ,  25 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 26 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,    700416 ,\r\n 27, TIDL_ConvolutionLayer         , Conv_37                                   1,   1 ,  1 ,  26 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 27 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,   1478656 ,\r\n 28, TIDL_EltWiseLayer             , Add_38                                    1,   2 ,  1 ,  27 , 24 ,  x ,  x ,  x ,  x ,  x ,  x , 28 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,      9728 ,\r\n 29, TIDL_BatchNormLayer           , Relu_39                                   1,   1 ,  1 ,  28 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 29 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,      9728 ,\r\n 30, TIDL_ConvolutionLayer         , Conv_40                                   1,   1 ,  1 ,  29 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 30 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,   1478656 ,\r\n 31, TIDL_ConvolutionLayer         , Conv_42                                   1,   1 ,  1 ,  30 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 31 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,    700416 ,\r\n 32, TIDL_ConvolutionLayer         , Conv_44                                   1,   1 ,  1 ,  31 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 32 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,   1478656 ,\r\n 33, TIDL_EltWiseLayer             , Add_45                                    1,   2 ,  1 ,  32 , 29 ,  x ,  x ,  x ,  x ,  x ,  x , 33 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,      9728 ,\r\n 34, TIDL_BatchNormLayer           , Relu_46                                   1,   1 ,  1 ,  33 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 34 ,       1 ,     152 ,       8 ,       8 ,       1 ,     152 ,       8 ,       8 ,      9728 ,\r\n 35, TIDL_ConvolutionLayer         , Conv_47                                   1,   1 ,  1 ,  34 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 35 ,       1 ,     152 ,       8 ,       8 ,       1 ,     368 ,       8 ,       8 ,   3579904 ,\r\n 36, TIDL_ConvolutionLayer         , Conv_49                                   1,   1 ,  1 ,  35 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 36 ,       1 ,     368 ,       8 ,       8 ,       1 ,     368 ,       4 ,       4 ,    423936 ,\r\n 37, TIDL_ConvolutionLayer         , Conv_51                                   1,   1 ,  1 ,  36 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 37 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 38, TIDL_ConvolutionLayer         , Conv_52                                   1,   1 ,  1 ,  34 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 38 ,       1 ,     152 ,       8 ,       8 ,       1 ,     368 ,       4 ,       4 ,    894976 ,\r\n 39, TIDL_EltWiseLayer             , Add_53                                    1,   2 ,  1 ,  37 , 38 ,  x ,  x ,  x ,  x ,  x ,  x , 39 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 40, TIDL_BatchNormLayer           , Relu_54                                   1,   1 ,  1 ,  39 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 40 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 41, TIDL_ConvolutionLayer         , Conv_55                                   1,   1 ,  1 ,  40 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 41 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 42, TIDL_ConvolutionLayer         , Conv_57                                   1,   1 ,  1 ,  41 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 42 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,    423936 ,\r\n 43, TIDL_ConvolutionLayer         , Conv_59                                   1,   1 ,  1 ,  42 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 43 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 44, TIDL_EltWiseLayer             , Add_60                                    1,   2 ,  1 ,  43 , 40 ,  x ,  x ,  x ,  x ,  x ,  x , 44 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 45, TIDL_BatchNormLayer           , Relu_61                                   1,   1 ,  1 ,  44 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 45 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 46, TIDL_ConvolutionLayer         , Conv_62                                   1,   1 ,  1 ,  45 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 46 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 47, TIDL_ConvolutionLayer         , Conv_64                                   1,   1 ,  1 ,  46 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 47 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,    423936 ,\r\n 48, TIDL_ConvolutionLayer         , Conv_66                                   1,   1 ,  1 ,  47 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 48 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 49, TIDL_EltWiseLayer             , Add_67                                    1,   2 ,  1 ,  48 , 45 ,  x ,  x ,  x ,  x ,  x ,  x , 49 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 50, TIDL_BatchNormLayer           , Relu_68                                   1,   1 ,  1 ,  49 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 50 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 51, TIDL_ConvolutionLayer         , Conv_69                                   1,   1 ,  1 ,  50 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 51 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 52, TIDL_ConvolutionLayer         , Conv_71                                   1,   1 ,  1 ,  51 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 52 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,    423936 ,\r\n 53, TIDL_ConvolutionLayer         , Conv_73                                   1,   1 ,  1 ,  52 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 53 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 54, TIDL_EltWiseLayer             , Add_74                                    1,   2 ,  1 ,  53 , 50 ,  x ,  x ,  x ,  x ,  x ,  x , 54 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 55, TIDL_BatchNormLayer           , Relu_75                                   1,   1 ,  1 ,  54 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 55 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 56, TIDL_ConvolutionLayer         , Conv_76                                   1,   1 ,  1 ,  55 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 56 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 57, TIDL_ConvolutionLayer         , Conv_78                                   1,   1 ,  1 ,  56 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 57 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,    423936 ,\r\n 58, TIDL_ConvolutionLayer         , Conv_80                                   1,   1 ,  1 ,  57 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 58 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 59, TIDL_EltWiseLayer             , Add_81                                    1,   2 ,  1 ,  58 , 55 ,  x ,  x ,  x ,  x ,  x ,  x , 59 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 60, TIDL_BatchNormLayer           , Relu_82                                   1,   1 ,  1 ,  59 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 60 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 61, TIDL_ConvolutionLayer         , Conv_83                                   1,   1 ,  1 ,  60 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 61 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 62, TIDL_ConvolutionLayer         , Conv_85                                   1,   1 ,  1 ,  61 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 62 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,    423936 ,\r\n 63, TIDL_ConvolutionLayer         , Conv_87                                   1,   1 ,  1 ,  62 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 63 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 64, TIDL_EltWiseLayer             , Add_88                                    1,   2 ,  1 ,  63 , 60 ,  x ,  x ,  x ,  x ,  x ,  x , 64 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 65, TIDL_BatchNormLayer           , Relu_89                                   1,   1 ,  1 ,  64 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 65 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 66, TIDL_ConvolutionLayer         , Conv_90                                   1,   1 ,  1 ,  65 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 66 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 67, TIDL_ConvolutionLayer         , Conv_92                                   1,   1 ,  1 ,  66 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 67 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,    423936 ,\r\n 68, TIDL_ConvolutionLayer         , Conv_94                                   1,   1 ,  1 ,  67 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 68 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,   2166784 ,\r\n 69, TIDL_EltWiseLayer             , Add_95                                    1,   2 ,  1 ,  68 , 65 ,  x ,  x ,  x ,  x ,  x ,  x , 69 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\n 70, TIDL_BatchNormLayer           , Relu_96                                   1,   1 ,  1 ,  69 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 70 ,       1 ,     368 ,       4 ,       4 ,       1 ,     368 ,       4 ,       4 ,      5888 ,\r\nTotal Giga Macs : 0.0567\r\n已复制         1 个文件。\r\n\r\nProcessing config file .\\tempDir\\qunat_stats_config.txt !\r\n  0, TIDL_DataLayer                ,  0,  -1 ,  1 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  0 ,    0 ,    0 ,    0 ,    0 ,    1 ,    3 ,   32 ,   32 ,\r\n  1, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  0 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  1 ,    1 ,    3 ,   32 ,   32 ,    1 ,   32 ,   32 ,   32 ,\r\n  2, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  1 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  2 ,    1 ,   32 ,   32 ,   32 ,    1 ,   24 ,   32 ,   32 ,\r\n  3, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  2 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  3 ,    1 ,   24 ,   32 ,   32 ,    1 ,   24 ,   16 ,   16 ,\r\n  4, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  3 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  4 ,    1 ,   24 ,   16 ,   16 ,    1 ,   24 ,   16 ,   16 ,\r\n  5, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  1 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  5 ,    1 ,   32 ,   32 ,   32 ,    1 ,   24 ,   16 ,   16 ,\r\n  6, TIDL_EltWiseLayer             ,  1,   2 ,  1 ,  4 ,  5 ,  x ,  x ,  x ,  x ,  x ,  x ,  6 ,    1 ,   24 ,   16 ,   16 ,    1 ,   24 ,   16 ,   16 ,\r\n  7, TIDL_BatchNormLayer           ,  1,   1 ,  1 ,  6 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  7 ,    1 ,   24 ,   16 ,   16 ,    1 ,   24 ,   16 ,   16 ,\r\n  8, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  7 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  8 ,    1 ,   24 ,   16 ,   16 ,    1 ,   56 ,   16 ,   16 ,\r\n  9, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  8 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  9 ,    1 ,   56 ,   16 ,   16 ,    1 ,   56 ,   16 ,   16 ,\r\n 10, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  9 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 10 ,    1 ,   56 ,   16 ,   16 ,    1 ,   56 ,   16 ,   16 ,\r\n 11, TIDL_ConvolutionLayer         ,  1,   1 ,  1 ,  7 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 11 ,    1 ,   24 ,   16 ,   16 ,    1 ,   56 ,   16 ,   16 ,\r\n 12, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 10 , 11 ,  x ,  x ,  x ,  x ,  x ,  x , 12 ,    1 ,   56 ,   16 ,   16 ,    1 ,   56 ,   16 ,   16 ,\r\n 13, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 12 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 13 ,    1 ,   56 ,   16 ,   16 ,    1 ,   56 ,   16 ,   16 ,\r\n 14, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 13 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 14 ,    1 ,   56 ,   16 ,   16 ,    1 ,  152 ,   16 ,   16 ,\r\n 15, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 14 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 15 ,    1 ,  152 ,   16 ,   16 ,    1 ,  152 ,    8 ,    8 ,\r\n 16, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 15 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 16 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 17, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 13 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 17 ,    1 ,   56 ,   16 ,   16 ,    1 ,  152 ,    8 ,    8 ,\r\n 18, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 16 , 17 ,  x ,  x ,  x ,  x ,  x ,  x , 18 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 19, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 18 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 19 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 20, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 19 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 20 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 21, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 20 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 21 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 22, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 21 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 22 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 23, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 22 , 19 ,  x ,  x ,  x ,  x ,  x ,  x , 23 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 24, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 23 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 24 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 25, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 24 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 25 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 26, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 25 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 26 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 27, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 26 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 27 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 28, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 27 , 24 ,  x ,  x ,  x ,  x ,  x ,  x , 28 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 29, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 28 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 29 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 30, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 29 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 30 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 31, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 30 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 31 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 32, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 31 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 32 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 33, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 32 , 29 ,  x ,  x ,  x ,  x ,  x ,  x , 33 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 34, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 33 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 34 ,    1 ,  152 ,    8 ,    8 ,    1 ,  152 ,    8 ,    8 ,\r\n 35, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 34 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 35 ,    1 ,  152 ,    8 ,    8 ,    1 ,  368 ,    8 ,    8 ,\r\n 36, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 35 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 36 ,    1 ,  368 ,    8 ,    8 ,    1 ,  368 ,    4 ,    4 ,\r\n 37, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 36 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 37 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 38, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 34 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 38 ,    1 ,  152 ,    8 ,    8 ,    1 ,  368 ,    4 ,    4 ,\r\n 39, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 37 , 38 ,  x ,  x ,  x ,  x ,  x ,  x , 39 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 40, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 39 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 40 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 41, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 40 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 41 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 42, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 41 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 42 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 43, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 42 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 43 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 44, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 43 , 40 ,  x ,  x ,  x ,  x ,  x ,  x , 44 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 45, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 44 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 45 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 46, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 45 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 46 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 47, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 46 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 47 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 48, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 47 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 48 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 49, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 48 , 45 ,  x ,  x ,  x ,  x ,  x ,  x , 49 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 50, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 49 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 50 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 51, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 50 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 51 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 52, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 51 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 52 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 53, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 52 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 53 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 54, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 53 , 50 ,  x ,  x ,  x ,  x ,  x ,  x , 54 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 55, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 54 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 55 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 56, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 55 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 56 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 57, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 56 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 57 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 58, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 57 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 58 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 59, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 58 , 55 ,  x ,  x ,  x ,  x ,  x ,  x , 59 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 60, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 59 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 60 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 61, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 60 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 61 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 62, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 61 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 62 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 63, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 62 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 63 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 64, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 63 , 60 ,  x ,  x ,  x ,  x ,  x ,  x , 64 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 65, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 64 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 65 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 66, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 65 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 66 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 67, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 66 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 67 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 68, TIDL_ConvolutionLayer         ,  1,   1 ,  1 , 67 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 68 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 69, TIDL_EltWiseLayer             ,  1,   2 ,  1 , 68 , 65 ,  x ,  x ,  x ,  x ,  x ,  x , 69 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 70, TIDL_BatchNormLayer           ,  1,   1 ,  1 , 69 ,  x ,  x ,  x ,  x ,  x ,  x ,  x , 70 ,    1 ,  368 ,    4 ,    4 ,    1 ,  368 ,    4 ,    4 ,\r\n 71, TIDL_DataLayer                ,  0,   1 , -1 , 70 ,  x ,  x ,  x ,  x ,  x ,  x ,  x ,  0 ,    1 ,  368 ,    4 ,    4 ,    0 ,    0 ,    0 ,    0 ,\r\nLayer ID    ,inBlkWidth  ,inBlkHeight ,inBlkPitch  ,outBlkWidth ,outBlkHeight,outBlkPitch ,numInChs    ,numOutChs   ,numProcInChs,numLclInChs ,numLclOutChs,numProcItrs ,numAccItrs  ,numHorBlock ,numVerBlock ,inBlkChPitch,outBlkChPitc,alignOrNot\r\n      1           40           34           40           32           32           32            3           32            3            1            8            1            3            1            1         1360         1024            1\r\n      2           32           32           32           32           32           32           32           24           32            7            8            1            5            1            1         1024         1024            1\r\n      3           40           36           40           16           16           16            8            8            8            4            8            1            2            1            1         1440          256            1\r\n      4           16           16           16           16           16           16           24           24           24            8            8            1            3            1            1          256          256            1\r\n      5           32           32           32           16           16           16           32           24           32            7            8            1            5            1            1         1024          256            1\r\n      8           16           16           16           16           16           16           24           56           24            8            8            1            3            1            1          256          256            1\r\n      9           24           18           24           16           16           16            8            8            8            4            8            1            2            1            1          432          256            1\r\n     10           16           16           16           16           16           16           56           56           56            8            8            1            7            1            1          256          256            1\r\n     11           16           16           16           16           16           16           24           56           24            8            8            1            3            1            1          256          256            1\r\n     14           16           16           16           16           16           16           56          152           56            8            8            1            7            1            1          256          256            1\r\n     15           40           20           40           16            8           16            8            8            8            4            8            1            2            1            1          800          128            1\r\n     16           16            8           16           16            8           16          152          152          152            8            8            1           19            1            1          128          128            1\r\n     17           32           16           32           16            8           16           56          152           56            8            8            1            7            1            1          512          128            1\r\n     20           16            8           16           16            8           16          152          152          152            8            8            1           19            1            1          128          128            1\r\n     21           24           10           24           16            8           16            8            8            8            4            8            1            2            1            1          240          128            1\r\n     22           16            8           16           16            8           16          152          152          152            8            8            1           19            1            1          128          128            1\r\n     25           16            8           16           16            8           16          152          152          152            8            8            1           19            1            1          128          128            1\r\n     26           24           10           24           16            8           16            8            8            8            4            8            1            2            1            1          240          128            1\r\n     27           16            8           16           16            8           16          152          152          152            8            8            1           19            1            1          128          128            1\r\n     30           16            8           16           16            8           16          152          152          152            8            8            1           19            1            1          128          128            1\r\n     31           24           10           24           16            8           16            8            8            8            4            8            1            2            1            1          240          128            1\r\n     32           16            8           16           16            8           16          152          152          152            8            8            1           19            1            1          128          128            1\r\n     35           16            8           16           16            8           16          152          368          152            8            8            1           19            1            1          128          128            1\r\n     36           40           12           40           16            4           16            8            8            8            4            8            1            2            1            1          480           64            1\r\n     37           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     38           32            8           32           16            4           16          152          368          152            8            8            1           19            1            1          256           64            1\r\n     41           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     42           24            6           24           16            4           16            8            8            8            4            8            1            2            1            1          144           64            1\r\n     43           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     46           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     47           24            6           24           16            4           16            8            8            8            4            8            1            2            1            1          144           64            1\r\n     48           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     51           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     52           24            6           24           16            4           16            8            8            8            4            8            1            2            1            1          144           64            1\r\n     53           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     56           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     57           24            6           24           16            4           16            8            8            8            4            8            1            2            1            1          144           64            1\r\n     58           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     61           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     62           24            6           24           16            4           16            8            8            8            4            8            1            2            1            1          144           64            1\r\n     63           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     66           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n     67           24            6           24           16            4           16            8            8            8            4            8            1            2            1            1          144           64            1\r\n     68           16            4           16           16            4           16          368          368          368            8            8            1           46            1            1           64           64            1\r\n\r\nProcessing Frame Number : 0\r\n\r\n Layer    1 : Out Q :      239 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.88,     1.17, Sparsity : -32.41\r\n Layer    2 : Out Q :      324 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.79,     0.88, Sparsity : -12.50\r\n Layer    3 : Out Q :      212 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.44,     0.44, Sparsity :   0.00\r\n Layer    4 : Out Q :       95 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.15,     0.15, Sparsity :   0.00\r\n Layer    5 : Out Q :      142 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.20,     0.22, Sparsity : -12.50\r\n Layer    6 : Out Q :       87 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer    7 : Out Q :      175 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer    8 : Out Q :      452 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.34,     0.34, Sparsity :   0.00\r\n Layer    9 : Out Q :      544 , TIDL_ConvolutionLayer, PASSED  #MMACs =     1.03,     1.03, Sparsity :   0.00\r\n Layer   10 : Out Q :      239 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.80,     0.80, Sparsity :   0.00\r\n Layer   11 : Out Q :      148 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.34,     0.34, Sparsity :   0.00\r\n Layer   12 : Out Q :      139 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.03,     0.03, Sparsity :   0.00\r\n Layer   13 : Out Q :      279 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   14 : Out Q :      512 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.18,     2.18, Sparsity :   0.00\r\n Layer   15 : Out Q :      570 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.70,     0.70, Sparsity :   0.00\r\n Layer   16 : Out Q :      236 , TIDL_ConvolutionLayer, PASSED  #MMACs =     1.48,     1.48, Sparsity :   0.00\r\n Layer   17 : Out Q :      235 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.54,     0.54, Sparsity :   0.00\r\n Layer   18 : Out Q :      124 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.02,     0.02, Sparsity :   0.00\r\n Layer   19 : Out Q :      249 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   20 : Out Q :      621 , TIDL_ConvolutionLayer, PASSED  #MMACs =     1.48,     1.48, Sparsity :   0.00\r\n Layer   21 : Out Q :      670 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.70,     0.70, Sparsity :   0.00\r\n Layer   22 : Out Q :      267 , TIDL_ConvolutionLayer, PASSED  #MMACs =     1.48,     1.48, Sparsity :   0.00\r\n Layer   23 : Out Q :      131 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.02,     0.02, Sparsity :   0.00\r\n Layer   24 : Out Q :      263 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   25 : Out Q :      680 , TIDL_ConvolutionLayer, PASSED  #MMACs =     1.48,     1.48, Sparsity :   0.00\r\n Layer   26 : Out Q :      271 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.70,     0.70, Sparsity :   0.00\r\n Layer   27 : Out Q :      224 , TIDL_ConvolutionLayer, PASSED  #MMACs =     1.48,     1.48, Sparsity :   0.00\r\n Layer   28 : Out Q :      158 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.02,     0.02, Sparsity :   0.00\r\n Layer   29 : Out Q :      317 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   30 : Out Q :      884 , TIDL_ConvolutionLayer, PASSED  #MMACs =     1.48,     1.48, Sparsity :   0.00\r\n Layer   31 : Out Q :      665 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.70,     0.70, Sparsity :   0.00\r\n Layer   32 : Out Q :      556 , TIDL_ConvolutionLayer, PASSED  #MMACs =     1.48,     1.48, Sparsity :   0.00\r\n Layer   33 : Out Q :      150 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.02,     0.02, Sparsity :   0.00\r\n Layer   34 : Out Q :      301 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   35 : Out Q :      870 , TIDL_ConvolutionLayer, PASSED  #MMACs =     3.58,     3.58, Sparsity :   0.00\r\n Layer   36 : Out Q :      409 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.42,     0.42, Sparsity :   0.00\r\n Layer   37 : Out Q :      230 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   38 : Out Q :      199 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.89,     0.89, Sparsity :   0.00\r\n Layer   39 : Out Q :      175 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   40 : Out Q :      351 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   41 : Out Q :     1196 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   42 : Out Q :      483 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.42,     0.42, Sparsity :   0.00\r\n Layer   43 : Out Q :      220 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   44 : Out Q :      158 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   45 : Out Q :      320 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   46 : Out Q :     1105 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   47 : Out Q :      459 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.42,     0.42, Sparsity :   0.00\r\n Layer   48 : Out Q :      137 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   49 : Out Q :      136 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   50 : Out Q :      287 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   51 : Out Q :     1024 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   52 : Out Q :      631 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.42,     0.42, Sparsity :   0.00\r\n Layer   53 : Out Q :      178 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   54 : Out Q :      109 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   55 : Out Q :      219 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   56 : Out Q :     1488 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   57 : Out Q :      450 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.42,     0.42, Sparsity :   0.00\r\n Layer   58 : Out Q :      143 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   59 : Out Q :      106 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   60 : Out Q :      213 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   61 : Out Q :     1198 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   62 : Out Q :      556 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.42,     0.42, Sparsity :   0.00\r\n Layer   63 : Out Q :      150 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   64 : Out Q :      102 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   65 : Out Q :      205 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   66 : Out Q :     1259 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   67 : Out Q :      749 , TIDL_ConvolutionLayer, PASSED  #MMACs =     0.42,     0.42, Sparsity :   0.00\r\n Layer   68 : Out Q :      181 , TIDL_ConvolutionLayer, PASSED  #MMACs =     2.17,     2.17, Sparsity :   0.00\r\n Layer   69 : Out Q :      103 , TIDL_EltWiseLayer,     PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00\r\n Layer   70 : Out Q :      207 , TIDL_BatchNormLayer  , PASSED  #MMACs =     0.01,     0.01, Sparsity :   0.00",
    "imageList" : null
  }, {
    "contentId" : "",
    "userName" : "lluo",
    "rankPoints" : "340",
    "rankName" : "Intellectual",
    "date" : "",
    "userId" : "/members/5927174",
    "content" : "Hi Praveen Is there any updates? lluo",
    "imageList" : null
  }, {
    "contentId" : "",
    "userName" : "Praveen Eppa1",
    "rankPoints" : "17580",
    "rankName" : "TI__Genius",
    "date" : "",
    "userId" : "/members/6019814",
    "content" : "Hi IIuo, I have looked at the log you shared, it looks okay to me, this configurations is supported on TDA2. Please follow steps mentioned in section 3.8 (Matching TIDL inference result) in the TIDL user guide. BTW, what is the TIDL release version that you are using? Thanks, Praveen",
    "imageList" : null
  }, {
    "contentId" : "",
    "userName" : "lluo",
    "rankPoints" : "340",
    "rankName" : "Intellectual",
    "date" : "",
    "userId" : "/members/5927174",
    "content" : "Hi Praveen I have been using REL.TIDL.01.01.03.00 . And the model & tools has been uploaded in regnet_import.zip. I will follow user guide to check my model on REL.TIDL.01.02.00.00 later.",
    "imageList" : null
  }, {
    "contentId" : "",
    "userName" : "Praveen Eppa1",
    "rankPoints" : "17580",
    "rankName" : "TI__Genius",
    "date" : "",
    "userId" : "/members/6019814",
    "content" : "Hi, Please check with REL.TIDL.01.02.00.00 and let us know. Thanks, Praveen",
    "imageList" : null
  } ],
  "tags" : [ ],
  "fourmType" : "processors-forum"
}