# How to connect the board for debugging ## Model Source The model used in this example come from the following open source projects: https://github.com/shicai/MobileNet-Caffe ## Script Usage *Usage:* ``` python test.py ``` *Description:* - The default target platform in script is 'rk3588', please modify the 'target_platform' parameter of 'rknn.config' according to the actual platform. - The 'target' parameter in 'rknn.init_runtime'/'rknn.accuracy_analysis' is set to 'rk3588'. - The 'perf_debug' and 'eval_mem' parameter of 'rknn.config' is set to True. ## Expected Results This example would output the following results: - Output encrypted rknn model: mobilenet_v2.crypt.rknn - List devices that connect to host ``` ************************* all device(s) with adb mode: rk3588 ************************* ``` - Print version ``` ============================================== RKNN VERSION: API: librknn_api version: 1.5.1 (7c1b999 build@2023-07-18T12:45:49) DRV: rknn_server: 1.5.0 (a4d9293 build: 2023-06-01 18:02:59) rknnrt: 1.5.1b19 (32afb0e92@2023-07-14T12:45:31) ============================================== ``` - Print perf information ``` =================================================================================================================== Performance #### The performance result is just for debugging, #### #### may worse than actual performance! #### =================================================================================================================== ID OpType DataType Target InputShape OutputShape DDR Cycles NPU Cycles Total Cycles Time(us) MacUsage(%) WorkLoad(0/1/2)-ImproveTherical Task Number Task Size Regcmd Size RW(KB) FullName 1 InputOperator UINT8 CPU \ (1,3,224,224) 0 0 0 21 \ 0.0%/0.0%/0.0% - Up:0.0% 0 0 0 147.00 InputOperator:data 2 ConvRelu UINT8 NPU (1,3,224,224),(32,3,3,3),(32) (1,32,112,112) 94150 10584 94150 375 2.82 0.0%/0.0%/0.0% - Up:0.0% 0 0 0 543.75 Conv:conv1 ... Total Operator Elapsed Time(us): 20274 Total Memory RW Amount(MB): 0 Operator Time-Consuming Ranking: OpType Call Number CPU Time(us) NPU Time(us) Total Time(us) Time Ratio(%) ConvRelu 36 0 13166 13166 64.94 ConvAdd 10 0 3677 3677 18.13 Conv 9 0 2878 2878 14.19 exSoftmax13 1 387 0 387 1.9 exDataConvert 1 0 120 120 0.59 OutputOperator 1 25 0 25 0.12 InputOperator 1 21 0 21 0.1 =================================================================================================================== ``` - Print memory information ``` ====================================================== Memory Profile Info Dump ====================================================== NPU model memory detail(bytes): Total Weight Memory: 3.53 MiB Total Internal Tensor Memory: 1.67 MiB Total Memory: 5.58 MiB INFO: When evaluating memory usage, we need consider the size of model, current model size is: 4.00 MiB ====================================================== ``` - Print the TOP5 labels and corresponding scores of the test image classification results ``` -----TOP 5----- [155]: 0.99462890625 [204]: 0.0019969940185546875 [154]: 0.0017757415771484375 [283]: 0.0006690025329589844 [284]: 0.0001976490020751953 ``` - Output the results of the accuracy analysis: ``` # simulator_error: calculate the simulator errors. # entire: errors between 'golden' and 'simulator'. # single: single layer errors. (compare to 'entire', the input of each layer is come from 'golden')! # runtime_error: calculate the runtime errors. # entire: errors between 'golden' and 'runtime'. # single_sim: single layer errors between 'simulator' and 'runtime'. # ('nan' means that tensor are 'all zeros', or 'all equal', or 'large values', etc) layer_name simulator_error runtime_error entire single entire single_sim ------------------------------------------------------------------------------ [Input] data 1.000000 1.000000 [exDataConvert] data_int8 0.999986 0.999986 [Conv] conv1/scale [Relu] relu1 0.999967 0.999967 0.999960 0.999942 [Conv] conv2_1/expand/scale [Relu] relu2_1/expand 0.999929 0.999940 0.999924 0.999998 [Conv] conv2_1/dwise/scale [Relu] relu2_1/dwise 0.999491 0.999585 0.999489 0.999998 [Conv] conv2_1/linear/scale 0.996095 0.999104 0.996004 0.999993 ... [Relu] relu6_4 0.979375 0.999889 0.982138 0.999999 [Conv] pool6 0.985147 0.999967 0.987996 1.000000 [Conv] fc7 0.982232 0.999905 0.984954 1.000000 [exDataConvert] fc7__float16 0.982232 0.999957 0.984955 1.000000 [exSoftmax13] prob 1.000000 1.000000 0.999999 1.000000 ``` - Note: Different platforms, different versions of tools and drivers may have slightly different results.