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TDA2EVM5777: after generating the Network and parameter Binary file in tidl

user6187828
Intellectual 600 points

Part Number: TDA2EVM5777

D:\PROCESSOR_SDK_VISION_03_07_00_00\ti_components\algorithms\REL.TIDL.01.01.03.00\modules\ti_dl\test\testvecs\config\import>tidl_model_import.out.exe tidl_import_j11_cifar.txt
Caffe Network File : D:\PROCESSOR_SDK_VISION_03_07_00_00\ti_components\algorithms\REL.TIDL.01.01.03.00\modules\ti_dl\test\testvecs\config\caffe_jacinto_models_caffe\trained\image_classification\cifar10_jacintonet11v2\sparse\deploy.prototxt
Caffe Model File : D:\PROCESSOR_SDK_VISION_03_07_00_00\ti_components\algorithms\REL.TIDL.01.01.03.00\modules\ti_dl\test\testvecs\config\caffe_jacinto_models_caffe\trained\image_classification\cifar10_jacintonet11v2\sparse\cifar10_jacintonet11v2_iter_64000.caffemodel
TIDL Network File : D:\PROCESSOR_SDK_VISION_03_07_00_00\ti_components\algorithms\REL.TIDL.01.01.03.00\modules\ti_dl\test\testvecs\config\tidl_models\tidl_net_cifar_jacintonet11v2.bin
TIDL Model File : D:\PROCESSOR_SDK_VISION_03_07_00_00\ti_components\algorithms\REL.TIDL.01.01.03.00\modules\ti_dl\test\testvecs\config\tidl_models\tidl_param_cifar_jacintonet11v2.bin
Name of the Network : jacintonet11v2_deploy
Num Inputs : 1
Num of Layer Detected : 17
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 ,
1, TIDL_BatchNormLayer , data/bias 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 32 , 32 , 1 , 3 , 32 , 32 , 3072 ,
2, TIDL_ConvolutionLayer , conv1a 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 32 , 32 , 1 , 32 , 32 , 32 , 2457600 ,
3, TIDL_ConvolutionLayer , conv1b 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 32 , 32 , 1 , 32 , 32 , 32 , 2359296 ,
4, TIDL_PoolingLayer , pool1 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 32 , 32 , 1 , 32 , 32 , 32 , 32768 ,
5, TIDL_ConvolutionLayer , res2a_branch2a 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 32 , 32 , 32 , 1 , 64 , 32 , 32 , 18874368 ,
6, TIDL_ConvolutionLayer , res2a_branch2b 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 32 , 32 , 1 , 64 , 16 , 16 , 9437184 ,
7, TIDL_ConvolutionLayer , res3a_branch2a 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 64 , 16 , 16 , 1 , 128 , 16 , 16 , 18874368 ,
8, TIDL_ConvolutionLayer , res3a_branch2b 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 16 , 16 , 1 , 128 , 16 , 16 , 9437184 ,
9, TIDL_PoolingLayer , pool3 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 16 , 16 , 1 , 128 , 16 , 16 , 32768 ,
10, TIDL_ConvolutionLayer , res4a_branch2a 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 128 , 16 , 16 , 1 , 256 , 16 , 16 , 75497472 ,
11, TIDL_ConvolutionLayer , res4a_branch2b 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 16 , 16 , 1 , 256 , 8 , 8 , 37748736 ,
12, TIDL_ConvolutionLayer , res5a_branch2a 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 256 , 8 , 8 , 1 , 512 , 8 , 8 , 75497472 ,
13, TIDL_ConvolutionLayer , res5a_branch2b 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 8 , 8 , 1 , 512 , 8 , 8 , 37748736 ,
14, TIDL_PoolingLayer , pool5 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 8 , 8 , 1 , 1 , 1 , 512 , 32768 ,
15, TIDL_InnerProductLayer , fc10 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 1 , 1 , 512 , 1 , 1 , 1 , 10 , 5120 ,
16, TIDL_SoftMaxLayer , prob 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 16 , 1 , 1 , 1 , 10 , 1 , 1 , 1 , 10 , 10 ,
Total Giga Macs : 0.2880
1 file(s) copied.

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 , 32 , 32 ,
1, TIDL_BatchNormLayer , 1, 1 , 1 , 0 , x , x , x , x , x , x , x , 1 , 1 , 3 , 32 , 32 , 1 , 3 , 32 , 32 ,
2, TIDL_ConvolutionLayer , 1, 1 , 1 , 1 , x , x , x , x , x , x , x , 2 , 1 , 3 , 32 , 32 , 1 , 32 , 32 , 32 ,
3, TIDL_ConvolutionLayer , 1, 1 , 1 , 2 , x , x , x , x , x , x , x , 3 , 1 , 32 , 32 , 32 , 1 , 32 , 32 , 32 ,
4, TIDL_PoolingLayer , 1, 1 , 1 , 3 , x , x , x , x , x , x , x , 4 , 1 , 32 , 32 , 32 , 1 , 32 , 32 , 32 ,
5, TIDL_ConvolutionLayer , 1, 1 , 1 , 4 , x , x , x , x , x , x , x , 5 , 1 , 32 , 32 , 32 , 1 , 64 , 32 , 32 ,
6, TIDL_ConvolutionLayer , 1, 1 , 1 , 5 , x , x , x , x , x , x , x , 6 , 1 , 64 , 32 , 32 , 1 , 64 , 16 , 16 ,
7, TIDL_ConvolutionLayer , 1, 1 , 1 , 6 , x , x , x , x , x , x , x , 7 , 1 , 64 , 16 , 16 , 1 , 128 , 16 , 16 ,
8, TIDL_ConvolutionLayer , 1, 1 , 1 , 7 , x , x , x , x , x , x , x , 8 , 1 , 128 , 16 , 16 , 1 , 128 , 16 , 16 ,
9, TIDL_PoolingLayer , 1, 1 , 1 , 8 , x , x , x , x , x , x , x , 9 , 1 , 128 , 16 , 16 , 1 , 128 , 16 , 16 ,
10, TIDL_ConvolutionLayer , 1, 1 , 1 , 9 , x , x , x , x , x , x , x , 10 , 1 , 128 , 16 , 16 , 1 , 256 , 16 , 16 ,
11, TIDL_ConvolutionLayer , 1, 1 , 1 , 10 , x , x , x , x , x , x , x , 11 , 1 , 256 , 16 , 16 , 1 , 256 , 8 , 8 ,
12, TIDL_ConvolutionLayer , 1, 1 , 1 , 11 , x , x , x , x , x , x , x , 12 , 1 , 256 , 8 , 8 , 1 , 512 , 8 , 8 ,
13, TIDL_ConvolutionLayer , 1, 1 , 1 , 12 , x , x , x , x , x , x , x , 13 , 1 , 512 , 8 , 8 , 1 , 512 , 8 , 8 ,
14, TIDL_PoolingLayer , 1, 1 , 1 , 13 , x , x , x , x , x , x , x , 14 , 1 , 512 , 8 , 8 , 1 , 1 , 1 , 512 ,
15, TIDL_InnerProductLayer , 1, 1 , 1 , 14 , x , x , x , x , x , x , x , 15 , 1 , 1 , 1 , 512 , 1 , 1 , 1 , 10 ,
16, TIDL_SoftMaxLayer , 1, 1 , 1 , 15 , x , x , x , x , x , x , x , 16 , 1 , 1 , 1 , 10 , 1 , 1 , 1 , 10 ,
17, TIDL_DataLayer , 0, 1 , -1 , 16 , x , x , x , x , x , x , x , 0 , 1 , 1 , 1 , 10 , 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 40 36 40 32 32 32 3 32 3 1 8 1 3 1 1 1440 1024 1
3 40 34 40 32 32 32 8 8 8 4 8 1 2 1 1 1360 1024 1
5 40 34 40 32 32 32 32 64 32 6 8 1 6 1 1 1360 1024 1
6 40 34 40 32 32 32 16 16 16 6 8 1 3 1 1 1360 1024 1
7 24 18 24 16 16 16 64 128 64 8 8 1 8 1 1 432 256 1
8 24 18 24 16 16 16 32 32 32 8 8 1 4 1 1 432 256 1
10 24 18 24 16 16 16 128 256 128 8 8 1 16 1 1 432 256 1
11 24 18 24 16 16 16 64 64 64 8 8 1 8 1 1 432 256 1
12 24 10 24 16 8 16 256 512 256 8 8 1 32 1 1 240 128 1
13 24 10 24 16 8 16 128 128 128 8 8 1 16 1 1 240 128 1

Processing Frame Number : 0

Layer 1 : Out Q : 274 , TIDL_BatchNormLayer , PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 2 : Out Q : 105261 , TIDL_ConvolutionLayer, PASSED #MMACs = 2.46, 1.60, Sparsity : 35.00
Layer 3 : Out Q : 87173 , TIDL_ConvolutionLayer, PASSED #MMACs = 2.36, 0.54, Sparsity : 76.91
Layer 4 :TIDL_PoolingLayer, PASSED #MMACs = 0.03, 0.03, Sparsity : 0.00
Layer 5 : Out Q : 128475 , TIDL_ConvolutionLayer, PASSED #MMACs = 18.87, 4.38, Sparsity : 76.78
Layer 6 : Out Q : 129594 , TIDL_ConvolutionLayer, PASSED #MMACs = 9.44, 2.18, Sparsity : 76.87
Layer 7 : Out Q : 173879 , TIDL_ConvolutionLayer, PASSED #MMACs = 18.87, 4.17, Sparsity : 77.88
Layer 8 : Out Q : 152680 , TIDL_ConvolutionLayer, PASSED #MMACs = 9.44, 2.09, Sparsity : 77.84
Layer 9 :TIDL_PoolingLayer, PASSED #MMACs = 0.03, 0.03, Sparsity : 0.00
Layer 10 : Out Q : 150324 , TIDL_ConvolutionLayer, PASSED #MMACs = 75.50, 16.63, Sparsity : 77.97
Layer 11 : Out Q : 116578 , TIDL_ConvolutionLayer, PASSED #MMACs = 37.75, 8.33, Sparsity : 77.94
Layer 12 : Out Q : 110726 , TIDL_ConvolutionLayer, PASSED #MMACs = 75.50, 16.66, Sparsity : 77.94
Layer 13 : Out Q : 17400 , TIDL_ConvolutionLayer, PASSED #MMACs = 37.75, 8.26, Sparsity : 78.11
Layer 14 : Out Q : 49706 , TIDL_PoolingLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 15 : Out Q : 3429 , TIDL_InnerProductLayer, PASSED #MMACs = 0.00, 0.00, Sparsity : 0.00
Layer 16 :-------Max Index 0 : 254 ------- #MMACs = 0.00, 0.00, Sparsity : 0.00
End of config list found !

please explain this i am not understanding what is deep leraning things

  • 0
    TI__Genius 17580 points

    Hi,

    This "tidl_import_j11_cifar.txt" does the image classification on the CIFAR10 dataset and the output is from last layer (Layer16) Max index 0 means the class label for the input image in the dataset.

    We would recommend to gain some knowledge about deep learning before using TIDL. Moving forward please post your questions regarding only TIDL issues. 

      

    Thanks,

    Praveen 

  • 0 in reply to Praveen Eppa1
    Prodigy 210 points

    Sir no need to train neural network from the scratch so we are getting the trained neural network so for some regarding what is the main using in python code I.e deploy.prototxt  please explain that code

  • 0 in reply to user6188127
    TI__Genius 17580 points

    Hi,

    As I pointed above, please let us know if you are facing any issues while using TIDL, we are happy to help. 

    For other general deep learning questions, please use online available forums like stack overflow etc..

    Thanks,

    Praveen