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{
"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"
} |