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Luckfox Pico Max运行RKNN-Toolkit2中的Yolov5 adb USB仿真

news 来源：原创 2025/9/17 18:13:46

1：下载rknn-toolkit2

git clone https://github.com/rockchip-linux/rknn-toolkit2

2：修改onnx目录下的yolov5的test.py的代码

# pre-process config
print('--> Config model')
rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], target_platform='rv1106') #target_platform='rk3566')

# Init runtime environment
print('--> Init runtime environment')
# ret = rknn.init_runtime()
ret = rknn.init_runtime(target='rv1106', device_id= 'bd547ee6900c058b')

3：adb push rknn_sever和依赖库

RV1103/RV1106上使用的RKNPU Runtime库是librknnmrt.so，使用32-bit的rknn_server,启动步骤如下：（armhf-uclibc）

adb push RV1106/Linux/rknn_server/armhf-uclibc/usr/bin下的所有文件到/oem/usr/bin目录
adb push RV1106/Linux/librknn_api/armhf-uclibc/librknnmrt.so到/oem/usr/lib目录
进入板子的串口终端，执行：

chmod +x /oem/usr/bin/rknn_server
chmod +x /oem/usr/bin/start_rknn.sh
chmod +x /oem/usr/bin/restart_rknn.sh
restart_rknn.sh

4：运行yolov5的python代码进行adb连接仿真

(RKNN-Toolkit2) ubuntu@ubuntu:~/Downloads/rknn-toolkit2-master/rknn-toolkit2/examples/onnx/yolov5$ python3 test.py 
W __init__: rknn-toolkit2 version: 1.6.0+81f21f4d
--> Config model
done
--> Loading model
W load_onnx: It is recommended onnx opset 19, but your onnx model opset is 12!
W load_onnx: Model converted from pytorch, 'opset_version' should be set 19 in torch.onnx.export for successful convert!
Loading : 100%|█████████████████████████████████████████████████| 125/125 [00:00<00:00, 6242.27it/s]
done
--> Building model
I base_optimize ...
I base_optimize done.
I 
I fold_constant ...
I fold_constant done.
I 
I correct_ops ...
I correct_ops done.
I 
I fuse_ops ...
I fuse_ops done.
I 
I sparse_weight ...
I sparse_weight done.
I 
GraphPreparing : 100%|██████████████████████████████████████████| 149/149 [00:00<00:00, 1538.03it/s]
Quantizating : 100%|██████████████████████████████████████████████| 149/149 [00:01<00:00, 85.94it/s]
I 
I quant_optimizer ...
I quant_optimizer results:
I     adjust_tanh_sigmoid: ['Sigmoid_146', 'Sigmoid_148', 'Sigmoid_150']
I     adjust_relu: ['Relu_144', 'Relu_141', 'Relu_139', 'Relu_137', 'Relu_135', 'Relu_132', 'Relu_130', 'Relu_127', 'Relu_125', 'Relu_123', 'Relu_121', 'Relu_118', 'Relu_116', 'Relu_113', 'Relu_111', 'Relu_109', 'Relu_107', 'Relu_102', 'Relu_100', 'Relu_97', 'Relu_95', 'Relu_93', 'Relu_91', 'Relu_86', 'Relu_84', 'Relu_75', 'Relu_73', 'Relu_70', 'Relu_67', 'Relu_65', 'Relu_63', 'Relu_61', 'Relu_59', 'Relu_56', 'Relu_53', 'Relu_51', 'Relu_48', 'Relu_46', 'Relu_43', 'Relu_41', 'Relu_39', 'Relu_37', 'Relu_35', 'Relu_32', 'Relu_29', 'Relu_27', 'Relu_24', 'Relu_22', 'Relu_20', 'Relu_18', 'Relu_16', 'Relu_13', 'Relu_10', 'Relu_8', 'Relu_6', 'Relu_4', 'Relu_2']
I     adjust_no_change_node: ['MaxPool_81', 'MaxPool_80']
I quant_optimizer done.
I 
W build: The default input dtype of 'images' is changed from 'float32' to 'int8' in rknn model for performance!Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of 'output' is changed from 'float32' to 'int8' in rknn model for performance!Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of '283' is changed from 'float32' to 'int8' in rknn model for performance!Please take care of this change when deploy rknn model with Runtime API!
W build: The default output dtype of '285' is changed from 'float32' to 'int8' in rknn model for performance!Please take care of this change when deploy rknn model with Runtime API!
I rknn building ...
I RKNN: [11:24:23.838] compress = 0, conv_eltwise_activation_fuse = 1, global_fuse = 1, multi-core-model-mode = 7, output_optimize = 1,enable_argb_group=0 ,layout_match = 1, pipeline_fuse = 0
I RKNN: librknnc version: 1.6.0 (585b3edcf@2023-12-11T07:42:56)
D RKNN: [11:24:24.052] RKNN is invoked
W RKNN: [11:24:24.721] Model initializer tensor data is empty, name: 219
W RKNN: [11:24:24.721] Model initializer tensor data is empty, name: 238
D RKNN: [11:24:24.748] >>>>>> start: rknn::RKNNExtractCustomOpAttrs
D RKNN: [11:24:24.749] <<<<<<<< end: rknn::RKNNExtractCustomOpAttrs
D RKNN: [11:24:24.749] >>>>>> start: rknn::RKNNSetOpTargetPass
D RKNN: [11:24:24.749] <<<<<<<< end: rknn::RKNNSetOpTargetPass
D RKNN: [11:24:24.749] >>>>>> start: rknn::RKNNBindNorm
D RKNN: [11:24:24.750] <<<<<<<< end: rknn::RKNNBindNorm
D RKNN: [11:24:24.750] >>>>>> start: rknn::RKNNAddFirstConv
D RKNN: [11:24:24.751] <<<<<<<< end: rknn::RKNNAddFirstConv
D RKNN: [11:24:24.751] >>>>>> start: rknn::RKNNEliminateQATDataConvert
D RKNN: [11:24:24.752] <<<<<<<< end: rknn::RKNNEliminateQATDataConvert
D RKNN: [11:24:24.752] >>>>>> start: rknn::RKNNTileGroupConv
D RKNN: [11:24:24.752] <<<<<<<< end: rknn::RKNNTileGroupConv
D RKNN: [11:24:24.752] >>>>>> start: rknn::RKNNTileFcBatchFuse
D RKNN: [11:24:24.752] <<<<<<<< end: rknn::RKNNTileFcBatchFuse
D RKNN: [11:24:24.752] >>>>>> start: rknn::RKNNAddConvBias
D RKNN: [11:24:24.754] <<<<<<<< end: rknn::RKNNAddConvBias
D RKNN: [11:24:24.754] >>>>>> start: rknn::RKNNTileChannel
D RKNN: [11:24:24.754] <<<<<<<< end: rknn::RKNNTileChannel
D RKNN: [11:24:24.754] >>>>>> start: rknn::RKNNPerChannelPrep
D RKNN: [11:24:24.754] <<<<<<<< end: rknn::RKNNPerChannelPrep
D RKNN: [11:24:24.754] >>>>>> start: rknn::RKNNBnQuant
D RKNN: [11:24:24.754] <<<<<<<< end: rknn::RKNNBnQuant
D RKNN: [11:24:24.754] >>>>>> start: rknn::RKNNFuseOptimizerPass
D RKNN: [11:24:24.866] <<<<<<<< end: rknn::RKNNFuseOptimizerPass
D RKNN: [11:24:24.866] >>>>>> start: rknn::RKNNTurnAutoPad
D RKNN: [11:24:24.866] <<<<<<<< end: rknn::RKNNTurnAutoPad
D RKNN: [11:24:24.866] >>>>>> start: rknn::RKNNInitRNNConst
D RKNN: [11:24:24.866] <<<<<<<< end: rknn::RKNNInitRNNConst
D RKNN: [11:24:24.866] >>>>>> start: rknn::RKNNInitCastConst
D RKNN: [11:24:24.866] <<<<<<<< end: rknn::RKNNInitCastConst
D RKNN: [11:24:24.866] >>>>>> start: rknn::RKNNMultiSurfacePass
D RKNN: [11:24:24.866] <<<<<<<< end: rknn::RKNNMultiSurfacePass
D RKNN: [11:24:24.866] >>>>>> start: rknn::RKNNReplaceConstantTensorPass
D RKNN: [11:24:24.867] <<<<<<<< end: rknn::RKNNReplaceConstantTensorPass
D RKNN: [11:24:24.867] >>>>>> start: OpEmit
D RKNN: [11:24:24.867] <<<<<<<< end: OpEmit
D RKNN: [11:24:24.867] >>>>>> start: rknn::RKNNLayoutMatchPass
I RKNN: [11:24:24.867] AppointLayout: t->setNativeLayout(64), tname:[128]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[131]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[133]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[142]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[137]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[140]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[143]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[145]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[147]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[161]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[151]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[156]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[159]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[162]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[166]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[185]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[170]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[175]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[180]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[183]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[186]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[190]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[199]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[194]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[197]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[200]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[202]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[204]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[205]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[206]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[207]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[208]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[209]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[210]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[211]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[213]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[221]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[223]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[229]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[225]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[227]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[230]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[232]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[240]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[242]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[248]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[244]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[246]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[249]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[251]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[253]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[254]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[256]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[262]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[258]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[260]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[263]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[265]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[267]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[268]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[270]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[276]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[272]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[274]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[277]
I RKNN: [11:24:24.868] AppointLayout: t->setNativeLayout(64), tname:[279]
D RKNN: [11:24:24.868] <<<<<<<< end: rknn::RKNNLayoutMatchPass
D RKNN: [11:24:24.868] >>>>>> start: rknn::RKNNAddSecondaryNode
D RKNN: [11:24:24.869] <<<<<<<< end: rknn::RKNNAddSecondaryNode
D RKNN: [11:24:24.869] >>>>>> start: OpEmit
D RKNN: [11:24:24.883] <<<<<<<< end: OpEmit
D RKNN: [11:24:24.883] >>>>>> start: rknn::RKNNProfileAnalysisPass
D RKNN: [11:24:24.884] <<<<<<<< end: rknn::RKNNProfileAnalysisPass
D RKNN: [11:24:24.888] >>>>>> start: rknn::RKNNOperatorIdGenPass
D RKNN: [11:24:24.888] <<<<<<<< end: rknn::RKNNOperatorIdGenPass
D RKNN: [11:24:24.888] >>>>>> start: rknn::RKNNWeightTransposePass
W RKNN: [11:24:25.219] Warning: Tensor 289 need paramter qtype, type is set to float16 by default!
W RKNN: [11:24:25.219] Warning: Tensor 219 need paramter qtype, type is set to float16 by default!
W RKNN: [11:24:25.220] Warning: Tensor 290 need paramter qtype, type is set to float16 by default!
W RKNN: [11:24:25.220] Warning: Tensor 238 need paramter qtype, type is set to float16 by default!
D RKNN: [11:24:25.220] <<<<<<<< end: rknn::RKNNWeightTransposePass
D RKNN: [11:24:25.220] >>>>>> start: rknn::RKNNCPUWeightTransposePass
D RKNN: [11:24:25.220] <<<<<<<< end: rknn::RKNNCPUWeightTransposePass
D RKNN: [11:24:25.220] >>>>>> start: rknn::RKNNModelBuildPass
D RKNN: [11:24:25.844] RKNNModelBuildPass: [Statistics]
D RKNN: [11:24:25.844] total_regcfg_size     :    266816
D RKNN: [11:24:25.844] total_diff_regcfg_size:    164280
D RKNN: [11:24:25.844] <<<<<<<< end: rknn::RKNNModelBuildPass
D RKNN: [11:24:25.844] >>>>>> start: rknn::RKNNModelRegCmdbuildPass
D RKNN: [11:24:25.846] ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
D RKNN: [11:24:25.846]                                                                 Network Layer Information Table                                                                 
D RKNN: [11:24:25.846] ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
D RKNN: [11:24:25.846] ID   OpType           DataType Target InputShape                               OutputShape            DDRCycles    NPUCycles    MaxCycles    TaskNumber   RW(KB)       FullName        
D RKNN: [11:24:25.846] ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
D RKNN: [11:24:25.846] 0    InputOperator    INT8     CPU    \                                        (1,3,640,640)          0            0            0            0/0          0            InputOperator:images
D RKNN: [11:24:25.846] 1    Conv             INT8     NPU    (1,3,640,640),(12,3,2,2),(12)            (1,12,320,320)         465543       409600       465543       8/0          1200         Conv:Conv_0     
D RKNN: [11:24:25.846] 2    ConvRelu         INT8     NPU    (1,12,320,320),(32,12,3,3),(32)          (1,32,320,320)         798774       1843200      1843200      8/0          1604         Conv:Conv_1     
D RKNN: [11:24:25.846] 3    ConvRelu         INT8     NPU    (1,32,320,320),(64,32,3,3),(64)          (1,64,160,160)         801060       921600       921600       16/0         3218         Conv:Conv_3     
D RKNN: [11:24:25.846] 4    ConvRelu         INT8     NPU    (1,64,160,160),(32,64,1,1),(32)          (1,32,160,160)         399366       204800       399366       8/0          1602         Conv:Conv_5     
D RKNN: [11:24:25.846] 5    ConvRelu         INT8     NPU    (1,64,160,160),(32,64,1,1),(32)          (1,32,160,160)         399366       204800       399366       8/0          1602         Conv:Conv_12    
D RKNN: [11:24:25.846] 6    ConvRelu         INT8     NPU    (1,32,160,160),(32,32,1,1),(32)          (1,32,160,160)         266203       204800       266203       4/0          801          Conv:Conv_7     
D RKNN: [11:24:25.846] 7    ConvReluAdd      INT8     NPU    (1,32,160,160),(32,32,3,3),(32),...      (1,32,160,160)         400530       460800       460800       4/0          1609         Conv:Conv_9     
D RKNN: [11:24:25.846] 8    Concat           INT8     NPU    (1,32,160,160),(1,32,160,160)            (1,64,160,160)         531990       0            531990       2/0          1600         Concat:Concat_14
D RKNN: [11:24:25.846] 9    ConvRelu         INT8     NPU    (1,64,160,160),(64,64,1,1),(64)          (1,64,160,160)         532738       409600       532738       8/0          1604         Conv:Conv_15    
D RKNN: [11:24:25.846] 10   ConvRelu         INT8     NPU    (1,64,160,160),(128,64,3,3),(128)        (1,128,80,80)          411128       921600       921600       9/0          1673         Conv:Conv_17    
D RKNN: [11:24:25.846] 11   ConvRelu         INT8     NPU    (1,128,80,80),(64,128,1,1),(64)          (1,64,80,80)           200910       102400       200910       4/0          808          Conv:Conv_19    
D RKNN: [11:24:25.846] 12   ConvRelu         INT8     NPU    (1,128,80,80),(64,128,1,1),(64)          (1,64,80,80)           200910       102400       200910       4/0          808          Conv:Conv_31    
D RKNN: [11:24:25.846] 13   ConvRelu         INT8     NPU    (1,64,80,80),(64,64,1,1),(64)            (1,64,80,80)           133746       102400       133746       2/0          404          Conv:Conv_21    
D RKNN: [11:24:25.846] 14   ConvReluAdd      INT8     NPU    (1,64,80,80),(64,64,3,3),(64),...        (1,64,80,80)           205564       460800       460800       3/0          836          Conv:Conv_23    
D RKNN: [11:24:25.846] 15   ConvRelu         INT8     NPU    (1,64,80,80),(64,64,1,1),(64)            (1,64,80,80)           133746       102400       133746       2/0          404          Conv:Conv_26    
D RKNN: [11:24:25.846] 16   ConvReluAdd      INT8     NPU    (1,64,80,80),(64,64,3,3),(64),...        (1,64,80,80)           205564       460800       460800       3/0          836          Conv:Conv_28    
D RKNN: [11:24:25.846] 17   Concat           INT8     NPU    (1,64,80,80),(1,64,80,80)                (1,128,80,80)          265995       0            265995       2/0          800          Concat:Concat_33
D RKNN: [11:24:25.846] 18   ConvRelu         INT8     NPU    (1,128,80,80),(128,128,1,1),(128)        (1,128,80,80)          268821       204800       268821       4/0          817          Conv:Conv_34    
D RKNN: [11:24:25.846] 19   ConvRelu         INT8     NPU    (1,128,80,80),(256,128,3,3),(256)        (1,256,40,40)          247708       921600       921600       5/0          1090         Conv:Conv_36    
D RKNN: [11:24:25.846] 20   ConvRelu         INT8     NPU    (1,256,40,40),(128,256,1,1),(128)        (1,128,40,40)          105235       102400       105235       2/0          433          Conv:Conv_38    
D RKNN: [11:24:25.846] 21   ConvRelu         INT8     NPU    (1,256,40,40),(128,256,1,1),(128)        (1,128,40,40)          105235       102400       105235       2/0          433          Conv:Conv_55    
D RKNN: [11:24:25.846] 22   ConvRelu         INT8     NPU    (1,128,40,40),(128,128,1,1),(128)        (1,128,40,40)          69325        51200        69325        1/0          217          Conv:Conv_40    
D RKNN: [11:24:25.846] 23   ConvReluAdd      INT8     NPU    (1,128,40,40),(128,128,3,3),(128),...    (1,128,40,40)          123854       460800       460800       2/0          545          Conv:Conv_42    
D RKNN: [11:24:25.846] 24   ConvRelu         INT8     NPU    (1,128,40,40),(128,128,1,1),(128)        (1,128,40,40)          69325        51200        69325        1/0          217          Conv:Conv_45    
D RKNN: [11:24:25.846] 25   ConvReluAdd      INT8     NPU    (1,128,40,40),(128,128,3,3),(128),...    (1,128,40,40)          123854       460800       460800       2/0          545          Conv:Conv_47    
D RKNN: [11:24:25.846] 26   ConvRelu         INT8     NPU    (1,128,40,40),(128,128,1,1),(128)        (1,128,40,40)          69325        51200        69325        1/0          217          Conv:Conv_50    
D RKNN: [11:24:25.846] 27   ConvReluAdd      INT8     NPU    (1,128,40,40),(128,128,3,3),(128),...    (1,128,40,40)          123854       460800       460800       2/0          545          Conv:Conv_52    
D RKNN: [11:24:25.846] 28   Concat           INT8     NPU    (1,128,40,40),(1,128,40,40)              (1,256,40,40)          132998       0            132998       2/0          400          Concat:Concat_57
D RKNN: [11:24:25.846] 29   ConvRelu         INT8     NPU    (1,256,40,40),(256,256,1,1),(256)        (1,256,40,40)          143970       204800       204800       3/0          466          Conv:Conv_58    
D RKNN: [11:24:25.846] 30   ConvRelu         INT8     NPU    (1,256,40,40),(512,256,3,3),(512)        (1,512,20,20)          291930       921600       921600       3/0          1556         Conv:Conv_60    
D RKNN: [11:24:25.846] 31   ConvRelu         INT8     NPU    (1,512,20,20),(256,512,1,1),(256)        (1,256,20,20)          71487        102400       102400       2/0          330          Conv:Conv_62    
D RKNN: [11:24:25.846] 32   ConvRelu         INT8     NPU    (1,512,20,20),(256,512,1,1),(256)        (1,256,20,20)          71487        102400       102400       2/0          330          Conv:Conv_69    
D RKNN: [11:24:25.846] 33   ConvRelu         INT8     NPU    (1,256,20,20),(256,256,1,1),(256)        (1,256,20,20)          44222        51200        51200        1/0          166          Conv:Conv_64    
D RKNN: [11:24:25.846] 34   ConvReluAdd      INT8     NPU    (1,256,20,20),(256,256,3,3),(256),...    (1,256,20,20)          145965       460800       460800       1/0          778          Conv:Conv_66    
D RKNN: [11:24:25.846] 35   Concat           INT8     NPU    (1,256,20,20),(1,256,20,20)              (1,512,20,20)          66499        0            66499        2/0          200          Concat:Concat_71
D RKNN: [11:24:25.846] 36   ConvRelu         INT8     NPU    (1,512,20,20),(512,512,1,1),(512)        (1,512,20,20)          109723       204800       204800       2/0          460          Conv:Conv_72    
D RKNN: [11:24:25.846] 37   ConvRelu         INT8     NPU    (1,512,20,20),(256,512,1,1),(256)        (1,256,20,20)          71487        102400       102400       2/0          330          Conv:Conv_74    
D RKNN: [11:24:25.846] 38   MaxPool          INT8     NPU    (1,256,20,20)                            (1,256,20,20)          33250        0            33250        1/0          100          MaxPool:MaxPool_76
D RKNN: [11:24:25.846] 39   MaxPool          INT8     NPU    (1,256,20,20)                            (1,256,20,20)          33250        0            33250        1/0          100          MaxPool:MaxPool_77
D RKNN: [11:24:25.846] 40   MaxPool          INT8     NPU    (1,256,20,20)                            (1,256,20,20)          33250        0            33250        1/0          100          MaxPool:MaxPool_78
D RKNN: [11:24:25.846] 41   MaxPool          INT8     NPU    (1,256,20,20)                            (1,256,20,20)          33250        0            33250        1/0          100          MaxPool:MaxPool_79
D RKNN: [11:24:25.846] 42   MaxPool          INT8     NPU    (1,256,20,20)                            (1,256,20,20)          33250        0            33250        1/0          100          MaxPool:MaxPool_80
D RKNN: [11:24:25.846] 43   MaxPool          INT8     NPU    (1,256,20,20)                            (1,256,20,20)          33250        0            33250        1/0          100          MaxPool:MaxPool_81
D RKNN: [11:24:25.846] 44   Concat           INT8     NPU    (1,256,20,20),(1,256,20,20),...          (1,1024,20,20)         132998       0            132998       4/0          400          Concat:Concat_82
D RKNN: [11:24:25.846] 45   ConvRelu         INT8     NPU    (1,1024,20,20),(512,1024,1,1),(512)      (1,512,20,20)          185532       409600       409600       3/0          916          Conv:Conv_83    
D RKNN: [11:24:25.846] 46   ConvRelu         INT8     NPU    (1,512,20,20),(256,512,1,1),(256)        (1,256,20,20)          71487        102400       102400       2/0          330          Conv:Conv_85    
D RKNN: [11:24:25.846] 47   Resize           INT8     NPU    (1,256,20,20),(0),(4)                    (1,256,40,40)          83126        0            83126        16/0         100          Resize:Resize_88
D RKNN: [11:24:25.846] 48   Concat           INT8     NPU    (1,256,40,40),(1,256,40,40)              (1,512,40,40)          265995       0            265995       2/0          800          Concat:Concat_89
D RKNN: [11:24:25.846] 49   ConvRelu         INT8     NPU    (1,512,40,40),(128,512,1,1),(128)        (1,128,40,40)          177053       204800       204800       5/0          865          Conv:Conv_90    
D RKNN: [11:24:25.846] 50   ConvRelu         INT8     NPU    (1,512,40,40),(128,512,1,1),(128)        (1,128,40,40)          177053       204800       204800       5/0          865          Conv:Conv_96    
D RKNN: [11:24:25.846] 51   ConvRelu         INT8     NPU    (1,128,40,40),(128,128,1,1),(128)        (1,128,40,40)          69325        51200        69325        1/0          217          Conv:Conv_92    
D RKNN: [11:24:25.846] 52   ConvRelu         INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          90605        460800       460800       2/0          345          Conv:Conv_94    
D RKNN: [11:24:25.846] 53   Concat           INT8     NPU    (1,128,40,40),(1,128,40,40)              (1,256,40,40)          132998       0            132998       2/0          400          Concat:Concat_98
D RKNN: [11:24:25.846] 54   ConvRelu         INT8     NPU    (1,256,40,40),(256,256,1,1),(256)        (1,256,40,40)          143970       204800       204800       3/0          466          Conv:Conv_99    
D RKNN: [11:24:25.846] 55   ConvRelu         INT8     NPU    (1,256,40,40),(128,256,1,1),(128)        (1,128,40,40)          105235       102400       105235       2/0          433          Conv:Conv_101   
D RKNN: [11:24:25.846] 56   Resize           INT8     NPU    (1,128,40,40),(0),(4)                    (1,128,80,80)          166250       0            166250       8/0          200          Resize:Resize_104
D RKNN: [11:24:25.846] 57   Concat           INT8     NPU    (1,128,80,80),(1,128,80,80)              (1,256,80,80)          531990       0            531990       2/0          1600         Concat:Concat_105
D RKNN: [11:24:25.846] 58   ConvRelu         INT8     NPU    (1,256,80,80),(64,256,1,1),(64)          (1,64,80,80)           335237       204800       335237       8/0          1616         Conv:Conv_106   
D RKNN: [11:24:25.846] 59   ConvRelu         INT8     NPU    (1,256,80,80),(64,256,1,1),(64)          (1,64,80,80)           335237       204800       335237       8/0          1616         Conv:Conv_112   
D RKNN: [11:24:25.846] 60   ConvRelu         INT8     NPU    (1,64,80,80),(64,64,1,1),(64)            (1,64,80,80)           133746       102400       133746       2/0          404          Conv:Conv_108   
D RKNN: [11:24:25.846] 61   ConvRelu         INT8     NPU    (1,64,80,80),(64,64,3,3),(64)            (1,64,80,80)           139066       460800       460800       3/0          436          Conv:Conv_110   
D RKNN: [11:24:25.846] 62   Concat           INT8     NPU    (1,64,80,80),(1,64,80,80)                (1,128,80,80)          265995       0            265995       2/0          800          Concat:Concat_114
D RKNN: [11:24:25.846] 63   ConvRelu         INT8     NPU    (1,128,80,80),(128,128,1,1),(128)        (1,128,80,80)          268821       204800       268821       4/0          817          Conv:Conv_115   
D RKNN: [11:24:25.846] 64   ConvRelu         INT8     NPU    (1,128,80,80),(128,128,3,3),(128)        (1,128,40,40)          190353       460800       460800       5/0          945          Conv:Conv_117   
D RKNN: [11:24:25.846] 65   Concat           INT8     NPU    (1,128,40,40),(1,128,40,40)              (1,256,40,40)          132998       0            132998       2/0          400          Concat:Concat_119
D RKNN: [11:24:25.846] 66   ConvSigmoid      INT8     NPU    (1,128,80,80),(255,128,1,1),(255)        (1,255,80,80)          404624       409600       409600       5/1          833          Conv:Conv_145   
D RKNN: [11:24:25.846] 67   ConvRelu         INT8     NPU    (1,256,40,40),(128,256,1,1),(128)        (1,128,40,40)          105235       102400       105235       2/0          433          Conv:Conv_120   
D RKNN: [11:24:25.846] 68   ConvRelu         INT8     NPU    (1,256,40,40),(128,256,1,1),(128)        (1,128,40,40)          105235       102400       105235       2/0          433          Conv:Conv_126   
D RKNN: [11:24:25.846] 69   ConvRelu         INT8     NPU    (1,128,40,40),(128,128,1,1),(128)        (1,128,40,40)          69325        51200        69325        1/0          217          Conv:Conv_122   
D RKNN: [11:24:25.846] 70   ConvRelu         INT8     NPU    (1,128,40,40),(128,128,3,3),(128)        (1,128,40,40)          90605        460800       460800       2/0          345          Conv:Conv_124   
D RKNN: [11:24:25.846] 71   Concat           INT8     NPU    (1,128,40,40),(1,128,40,40)              (1,256,40,40)          132998       0            132998       2/0          400          Concat:Concat_128
D RKNN: [11:24:25.846] 72   ConvRelu         INT8     NPU    (1,256,40,40),(256,256,1,1),(256)        (1,256,40,40)          143970       204800       204800       3/0          466          Conv:Conv_129   
D RKNN: [11:24:25.846] 73   ConvRelu         INT8     NPU    (1,256,40,40),(256,256,3,3),(256)        (1,256,20,20)          179214       460800       460800       3/0          978          Conv:Conv_131   
D RKNN: [11:24:25.846] 74   Concat           INT8     NPU    (1,256,20,20),(1,256,20,20)              (1,512,20,20)          66499        0            66499        2/0          200          Concat:Concat_133
D RKNN: [11:24:25.846] 75   ConvSigmoid      INT8     NPU    (1,256,40,40),(255,256,1,1),(255)        (1,255,40,40)          143929       204800       204800       4/1          465          Conv:Conv_147   
D RKNN: [11:24:25.846] 76   ConvRelu         INT8     NPU    (1,512,20,20),(256,512,1,1),(256)        (1,256,20,20)          71487        102400       102400       2/0          330          Conv:Conv_134   
D RKNN: [11:24:25.846] 77   ConvRelu         INT8     NPU    (1,512,20,20),(256,512,1,1),(256)        (1,256,20,20)          71487        102400       102400       2/0          330          Conv:Conv_140   
D RKNN: [11:24:25.846] 78   ConvRelu         INT8     NPU    (1,256,20,20),(256,256,1,1),(256)        (1,256,20,20)          44222        51200        51200        1/0          166          Conv:Conv_136   
D RKNN: [11:24:25.846] 79   ConvRelu         INT8     NPU    (1,256,20,20),(256,256,3,3),(256)        (1,256,20,20)          129340       460800       460800       1/0          678          Conv:Conv_138   
D RKNN: [11:24:25.846] 80   Concat           INT8     NPU    (1,256,20,20),(1,256,20,20)              (1,512,20,20)          66499        0            66499        2/0          200          Concat:Concat_142
D RKNN: [11:24:25.846] 81   ConvRelu         INT8     NPU    (1,512,20,20),(512,512,1,1),(512)        (1,512,20,20)          109723       204800       204800       2/0          460          Conv:Conv_143   
D RKNN: [11:24:25.846] 82   ConvSigmoid      INT8     NPU    (1,512,20,20),(255,512,1,1),(255)        (1,255,20,20)          71403        102400       102400       3/1          329          Conv:Conv_149   
D RKNN: [11:24:25.846] 83   OutputOperator   INT8     NPU    (1,255,80,80),(1,80,80,256)              \                      531990       0            531990       21/0         3200         OutputOperator:output
D RKNN: [11:24:25.846] 84   OutputOperator   INT8     NPU    (1,255,40,40),(1,40,40,256)              \                      146298       0            146298       8/0          880          OutputOperator:283
D RKNN: [11:24:25.846] 85   OutputOperator   INT8     NPU    (1,255,20,20),(1,20,20,256)              \                      43225        0            43225        4/0          260          OutputOperator:285
D RKNN: [11:24:25.846] ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
D RKNN: [11:24:25.847] <<<<<<<< end: rknn::RKNNModelRegCmdbuildPass
D RKNN: [11:24:25.848] >>>>>> start: rknn::RKNNFlatcModelBuildPass
D RKNN: [11:24:25.868] Export Mini RKNN model to /tmp/tmpmzghee_t/dumps/torch-jit-export.mini.rknn
D RKNN: [11:24:25.871] >>>>>> end: rknn::RKNNFlatcModelBuildPass
D RKNN: [11:24:25.872] >>>>>> start: rknn::RKNNMemStatisticsPass
D RKNN: [11:24:25.873] --------------------------------------------------------------------------------------------------------------------------------
D RKNN: [11:24:25.873]                                           Feature Tensor Information Table                                
D RKNN: [11:24:25.873] ----------------------------------------------------------------------------------------------+---------------------------------
D RKNN: [11:24:25.873] ID  User           Tensor              DataType  DataFormat   OrigShape      NativeShape      |     [Start       End)       Size
D RKNN: [11:24:25.873] ----------------------------------------------------------------------------------------------+---------------------------------
D RKNN: [11:24:25.873] 1   Conv           images              INT8      NC1HWC2      (1,3,640,640)  (1,1,640,640,3)  | 0x007297c0 0x008557c0 0x0012c000
D RKNN: [11:24:25.873] 2   ConvRelu       128                 INT8      NC1HWC2      (1,12,320,320) (1,1,320,320,16) | 0x008557c0 0x009e57c0 0x00190000
D RKNN: [11:24:25.873] 3   ConvRelu       131                 INT8      NC1HWC2      (1,32,320,320) (1,2,320,320,16) | 0x009e57c0 0x00d057c0 0x00320000
D RKNN: [11:24:25.873] 4   ConvRelu       133                 INT8      NC1HWC2      (1,64,160,160) (1,4,160,160,16) | 0x007297c0 0x008b97c0 0x00190000
D RKNN: [11:24:25.873] 5   ConvRelu       133                 INT8      NC1HWC2      (1,64,160,160) (1,4,160,160,16) | 0x007297c0 0x008b97c0 0x00190000
D RKNN: [11:24:25.873] 6   ConvRelu       135                 INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x008b97c0 0x009817c0 0x000c8000
D RKNN: [11:24:25.873] 7   ConvReluAdd    137                 INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x007297c0 0x007f17c0 0x000c8000
D RKNN: [11:24:25.873] 7   ConvReluAdd    135                 INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x008b97c0 0x009817c0 0x000c8000
D RKNN: [11:24:25.873] 8   Concat         140                 INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x007f17c0 0x008b97c0 0x000c8000
D RKNN: [11:24:25.873] 8   Concat         142                 INT8      NC1HWC2      (1,32,160,160) (1,2,160,160,16) | 0x009817c0 0x00a497c0 0x000c8000
D RKNN: [11:24:25.873] 9   ConvRelu       143                 INT8      NC1HWC2      (1,64,160,160) (1,4,160,160,16) | 0x00a497c0 0x00bd97c0 0x00190000
D RKNN: [11:24:25.873] 10  ConvRelu       145                 INT8      NC1HWC2      (1,64,160,160) (1,4,160,160,16) | 0x007297c0 0x008b97c0 0x00190000
D RKNN: [11:24:25.873] 11  ConvRelu       147                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x008b97c0 0x009817c0 0x000c8000
D RKNN: [11:24:25.873] 12  ConvRelu       147                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x008b97c0 0x009817c0 0x000c8000
D RKNN: [11:24:25.873] 13  ConvRelu       149                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x007297c0 0x0078d7c0 0x00064000
D RKNN: [11:24:25.873] 14  ConvReluAdd    151                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x007f17c0 0x008557c0 0x00064000
D RKNN: [11:24:25.873] 14  ConvReluAdd    149                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x007297c0 0x0078d7c0 0x00064000
D RKNN: [11:24:25.873] 15  ConvRelu       154                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x008557c0 0x008b97c0 0x00064000
D RKNN: [11:24:25.873] 16  ConvReluAdd    156                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x007297c0 0x0078d7c0 0x00064000
D RKNN: [11:24:25.873] 16  ConvReluAdd    154                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x008557c0 0x008b97c0 0x00064000
D RKNN: [11:24:25.873] 17  Concat         159                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x007f17c0 0x008557c0 0x00064000
D RKNN: [11:24:25.873] 17  Concat         161                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x0078d7c0 0x007f17c0 0x00064000
D RKNN: [11:24:25.873] 18  ConvRelu       162                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x008557c0 0x0091d7c0 0x000c8000
D RKNN: [11:24:25.873] 19  ConvRelu       164                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x007297c0 0x007f17c0 0x000c8000
D RKNN: [11:24:25.873] 20  ConvRelu       166                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x007f17c0 0x008557c0 0x00064000
D RKNN: [11:24:25.873] 21  ConvRelu       166                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x007f17c0 0x008557c0 0x00064000
D RKNN: [11:24:25.873] 22  ConvRelu       168                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008557c0 0x008877c0 0x00032000
D RKNN: [11:24:25.873] 23  ConvReluAdd    170                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x007f17c0 0x008237c0 0x00032000
D RKNN: [11:24:25.873] 23  ConvReluAdd    168                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008557c0 0x008877c0 0x00032000
D RKNN: [11:24:25.873] 24  ConvRelu       173                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008237c0 0x008557c0 0x00032000
D RKNN: [11:24:25.873] 25  ConvReluAdd    175                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x007f17c0 0x008237c0 0x00032000
D RKNN: [11:24:25.873] 25  ConvReluAdd    173                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008237c0 0x008557c0 0x00032000
D RKNN: [11:24:25.873] 26  ConvRelu       178                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008557c0 0x008877c0 0x00032000
D RKNN: [11:24:25.873] 27  ConvReluAdd    180                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x007f17c0 0x008237c0 0x00032000
D RKNN: [11:24:25.873] 27  ConvReluAdd    178                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008557c0 0x008877c0 0x00032000
D RKNN: [11:24:25.873] 28  Concat         183                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008237c0 0x008557c0 0x00032000
D RKNN: [11:24:25.873] 28  Concat         185                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008877c0 0x008b97c0 0x00032000
D RKNN: [11:24:25.873] 29  ConvRelu       186                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x008b97c0 0x0091d7c0 0x00064000
D RKNN: [11:24:25.873] 30  ConvRelu       188                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x007f17c0 0x008557c0 0x00064000
D RKNN: [11:24:25.873] 31  ConvRelu       190                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x008557c0 0x008877c0 0x00032000
D RKNN: [11:24:25.873] 32  ConvRelu       190                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x008557c0 0x008877c0 0x00032000
D RKNN: [11:24:25.873] 33  ConvRelu       192                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008877c0 0x008a07c0 0x00019000
D RKNN: [11:24:25.873] 34  ConvReluAdd    194                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008557c0 0x0086e7c0 0x00019000
D RKNN: [11:24:25.873] 34  ConvReluAdd    192                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008877c0 0x008a07c0 0x00019000
D RKNN: [11:24:25.873] 35  Concat         197                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0086e7c0 0x008877c0 0x00019000
D RKNN: [11:24:25.873] 35  Concat         199                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008a07c0 0x008b97c0 0x00019000
D RKNN: [11:24:25.873] 36  ConvRelu       200                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x008b97c0 0x008eb7c0 0x00032000
D RKNN: [11:24:25.873] 37  ConvRelu       202                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x008557c0 0x008877c0 0x00032000
D RKNN: [11:24:25.873] 38  MaxPool        204                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008877c0 0x008a07c0 0x00019000
D RKNN: [11:24:25.873] 39  MaxPool        205                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008557c0 0x0086e7c0 0x00019000
D RKNN: [11:24:25.873] 40  MaxPool        206                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0086e7c0 0x008877c0 0x00019000
D RKNN: [11:24:25.873] 41  MaxPool        207                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008557c0 0x0086e7c0 0x00019000
D RKNN: [11:24:25.873] 42  MaxPool        208                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008a07c0 0x008b97c0 0x00019000
D RKNN: [11:24:25.873] 43  MaxPool        209                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008557c0 0x0086e7c0 0x00019000
D RKNN: [11:24:25.873] 44  Concat         204                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008877c0 0x008a07c0 0x00019000
D RKNN: [11:24:25.873] 44  Concat         206                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0086e7c0 0x008877c0 0x00019000
D RKNN: [11:24:25.873] 44  Concat         208                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008a07c0 0x008b97c0 0x00019000
D RKNN: [11:24:25.873] 44  Concat         210                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008b97c0 0x008d27c0 0x00019000
D RKNN: [11:24:25.873] 45  ConvRelu       211                 INT8      NC1HWC2      (1,1024,20,20) (1,64,20,20,16)  | 0x008d27c0 0x009367c0 0x00064000
D RKNN: [11:24:25.873] 46  ConvRelu       213                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x008557c0 0x008877c0 0x00032000
D RKNN: [11:24:25.873] 47  Resize         215                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008877c0 0x008a07c0 0x00019000
D RKNN: [11:24:25.873] 48  Concat         220                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x008a07c0 0x009047c0 0x00064000
D RKNN: [11:24:25.873] 48  Concat         188                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x007f17c0 0x008557c0 0x00064000
D RKNN: [11:24:25.873] 49  ConvRelu       221                 INT8      NC1HWC2      (1,512,40,40)  (1,32,40,40,16)  | 0x009047c0 0x009cc7c0 0x000c8000
D RKNN: [11:24:25.873] 50  ConvRelu       221                 INT8      NC1HWC2      (1,512,40,40)  (1,32,40,40,16)  | 0x009047c0 0x009cc7c0 0x000c8000
D RKNN: [11:24:25.873] 51  ConvRelu       223                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008a07c0 0x008d27c0 0x00032000
D RKNN: [11:24:25.873] 52  ConvRelu       225                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x007f17c0 0x008237c0 0x00032000
D RKNN: [11:24:25.873] 53  Concat         227                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008a07c0 0x008d27c0 0x00032000
D RKNN: [11:24:25.873] 53  Concat         229                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008d27c0 0x009047c0 0x00032000
D RKNN: [11:24:25.873] 54  ConvRelu       230                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x007f17c0 0x008557c0 0x00064000
D RKNN: [11:24:25.873] 55  ConvRelu       232                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x008a07c0 0x009047c0 0x00064000
D RKNN: [11:24:25.873] 56  Resize         234                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x007f17c0 0x008237c0 0x00032000
D RKNN: [11:24:25.873] 57  Concat         239                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x008a07c0 0x009687c0 0x000c8000
D RKNN: [11:24:25.873] 57  Concat         164                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x007297c0 0x007f17c0 0x000c8000
D RKNN: [11:24:25.873] 58  ConvRelu       240                 INT8      NC1HWC2      (1,256,80,80)  (1,16,80,80,16)  | 0x009687c0 0x00af87c0 0x00190000
D RKNN: [11:24:25.873] 59  ConvRelu       240                 INT8      NC1HWC2      (1,256,80,80)  (1,16,80,80,16)  | 0x009687c0 0x00af87c0 0x00190000
D RKNN: [11:24:25.873] 60  ConvRelu       242                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x008237c0 0x008877c0 0x00064000
D RKNN: [11:24:25.873] 61  ConvRelu       244                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x0078d7c0 0x007f17c0 0x00064000
D RKNN: [11:24:25.873] 62  Concat         246                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x008237c0 0x008877c0 0x00064000
D RKNN: [11:24:25.873] 62  Concat         248                 INT8      NC1HWC2      (1,64,80,80)   (1,4,80,80,16)   | 0x007297c0 0x0078d7c0 0x00064000
D RKNN: [11:24:25.873] 63  ConvRelu       249                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x008a07c0 0x009687c0 0x000c8000
D RKNN: [11:24:25.873] 64  ConvRelu       251                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x007297c0 0x007f17c0 0x000c8000
D RKNN: [11:24:25.873] 65  Concat         253                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008237c0 0x008557c0 0x00032000
D RKNN: [11:24:25.873] 65  Concat         234                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x007f17c0 0x008237c0 0x00032000
D RKNN: [11:24:25.873] 66  ConvSigmoid    251                 INT8      NC1HWC2      (1,128,80,80)  (1,8,80,80,16)   | 0x007297c0 0x007f17c0 0x000c8000
D RKNN: [11:24:25.873] 67  ConvRelu       254                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x008a07c0 0x009047c0 0x00064000
D RKNN: [11:24:25.873] 68  ConvRelu       254                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x008a07c0 0x009047c0 0x00064000
D RKNN: [11:24:25.873] 69  ConvRelu       256                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x007297c0 0x0075b7c0 0x00032000
D RKNN: [11:24:25.873] 70  ConvRelu       258                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x008a07c0 0x008d27c0 0x00032000
D RKNN: [11:24:25.873] 71  Concat         260                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x007297c0 0x0075b7c0 0x00032000
D RKNN: [11:24:25.873] 71  Concat         262                 INT8      NC1HWC2      (1,128,40,40)  (1,8,40,40,16)   | 0x0075b7c0 0x0078d7c0 0x00032000
D RKNN: [11:24:25.873] 72  ConvRelu       263                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x008a07c0 0x009047c0 0x00064000
D RKNN: [11:24:25.873] 73  ConvRelu       265                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x007297c0 0x0078d7c0 0x00064000
D RKNN: [11:24:25.873] 74  Concat         267                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008a07c0 0x008b97c0 0x00019000
D RKNN: [11:24:25.873] 74  Concat         215                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008877c0 0x008a07c0 0x00019000
D RKNN: [11:24:25.873] 75  ConvSigmoid    265                 INT8      NC1HWC2      (1,256,40,40)  (1,16,40,40,16)  | 0x007297c0 0x0078d7c0 0x00064000
D RKNN: [11:24:25.873] 76  ConvRelu       268                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x008b97c0 0x008eb7c0 0x00032000
D RKNN: [11:24:25.873] 77  ConvRelu       268                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x008b97c0 0x008eb7c0 0x00032000
D RKNN: [11:24:25.873] 78  ConvRelu       270                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x008eb7c0 0x009047c0 0x00019000
D RKNN: [11:24:25.873] 79  ConvRelu       272                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x007427c0 0x0075b7c0 0x00019000
D RKNN: [11:24:25.873] 80  Concat         274                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x0075b7c0 0x007747c0 0x00019000
D RKNN: [11:24:25.873] 80  Concat         276                 INT8      NC1HWC2      (1,256,20,20)  (1,16,20,20,16)  | 0x007297c0 0x007427c0 0x00019000
D RKNN: [11:24:25.873] 81  ConvRelu       277                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x007f17c0 0x008237c0 0x00032000
D RKNN: [11:24:25.873] 82  ConvSigmoid    279                 INT8      NC1HWC2      (1,512,20,20)  (1,32,20,20,16)  | 0x007297c0 0x0075b7c0 0x00032000
D RKNN: [11:24:25.873] 83  OutputOperator output              INT8      NC1HWC2      (1,255,80,80)  (1,16,80,80,16)  | 0x009047c0 0x00a947c0 0x00190000
D RKNN: [11:24:25.873] 83  OutputOperator output_exSecondary0 INT8      NC1HWC2      (1,80,80,256)  (1,5,80,256,16)  | 0x00a947c0 0x00c247c0 0x00190000
D RKNN: [11:24:25.873] 83  OutputOperator output_exSecondary  INT8      NHWC         (1,80,80,255)  (1,80,80,255)    | 0x00c247c0 0x00db2ec0 0x0018e700
D RKNN: [11:24:25.873] 84  OutputOperator 283                 INT8      NC1HWC2      (1,255,40,40)  (1,16,40,40,16)  | 0x0078d7c0 0x007f17c0 0x00064000
D RKNN: [11:24:25.873] 84  OutputOperator 283_exSecondary0    INT8      NC1HWC2      (1,40,40,256)  (1,2,40,256,16)  | 0x007f17c0 0x008557c0 0x00064000
D RKNN: [11:24:25.873] 84  OutputOperator 283_exSecondary     INT8      NHWC         (1,40,40,255)  (1,40,40,255)    | 0x008557c0 0x008b9180 0x000639c0
D RKNN: [11:24:25.873] 85  OutputOperator 285                 INT8      NC1HWC2      (1,255,20,20)  (1,16,20,20,16)  | 0x0075b7c0 0x007747c0 0x00019000
D RKNN: [11:24:25.873] 85  OutputOperator 285_exSecondary0    INT8      NC1HWC2      (1,20,20,256)  (1,1,20,256,16)  | 0x007747c0 0x0078d7c0 0x00019000
D RKNN: [11:24:25.873] 85  OutputOperator 285_exSecondary     INT8      NHWC         (1,20,20,255)  (1,20,20,255)    | 0x007297c0 0x00742630 0x00018e70
D RKNN: [11:24:25.873] ----------------------------------------------------------------------------------------------+---------------------------------
D RKNN: [11:24:25.873] --------------------------------------------------------------------------------------------------------
D RKNN: [11:24:25.873]                                   Const Tensor Information Table                      
D RKNN: [11:24:25.873] ----------------------------------------------------------------------+---------------------------------
D RKNN: [11:24:25.873] ID  User        Tensor                       DataType  OrigShape      |     [Start       End)       Size
D RKNN: [11:24:25.873] ----------------------------------------------------------------------+---------------------------------
D RKNN: [11:24:25.873] 1   Conv        model.0.convsp.weight        INT8      (12,3,2,2)     | 0x00000000 0x000000c0 0x000000c0
D RKNN: [11:24:25.873] 1   Conv        model.0.convsp.weight_bias_0 INT32     (12)           | 0x006f6140*0x006f61c0 0x00000080
D RKNN: [11:24:25.873] 2   ConvRelu    287                          INT8      (32,12,3,3)    | 0x006f4d40 0x006f5f40 0x00001200
D RKNN: [11:24:25.873] 2   ConvRelu    288                          INT32     (32)           | 0x006f5f40 0x006f6040 0x00000100
D RKNN: [11:24:25.873] 3   ConvRelu    model.1.conv.weight          INT8      (64,32,3,3)    | 0x000000c0 0x000048c0 0x00004800
D RKNN: [11:24:25.873] 3   ConvRelu    model.1.conv.bias            INT32     (64)           | 0x000048c0 0x00004ac0 0x00000200
D RKNN: [11:24:25.873] 4   ConvRelu    model.2.cv1.conv.weight      INT8      (32,64,1,1)    | 0x00004ac0 0x000052c0 0x00000800
D RKNN: [11:24:25.873] 4   ConvRelu    model.2.cv1.conv.bias        INT32     (32)           | 0x000052c0 0x000053c0 0x00000100
D RKNN: [11:24:25.873] 5   ConvRelu    model.2.cv2.conv.weight      INT8      (32,64,1,1)    | 0x000053c0 0x00005bc0 0x00000800
D RKNN: [11:24:25.873] 5   ConvRelu    model.2.cv2.conv.bias        INT32     (32)           | 0x00005bc0 0x00005cc0 0x00000100
D RKNN: [11:24:25.873] 6   ConvRelu    model.2.m.0.cv1.conv.weight  INT8      (32,32,1,1)    | 0x00006ec0 0x000072c0 0x00000400
D RKNN: [11:24:25.873] 6   ConvRelu    model.2.m.0.cv1.conv.bias    INT32     (32)           | 0x000072c0 0x000073c0 0x00000100
D RKNN: [11:24:25.873] 7   ConvReluAdd model.2.m.0.cv2.conv.weight  INT8      (32,32,3,3)    | 0x000073c0 0x000097c0 0x00002400
D RKNN: [11:24:25.873] 7   ConvReluAdd model.2.m.0.cv2.conv.bias    INT32     (32)           | 0x000097c0 0x000098c0 0x00000100
D RKNN: [11:24:25.873] 9   ConvRelu    model.2.cv3.conv.weight      INT8      (64,64,1,1)    | 0x00005cc0 0x00006cc0 0x00001000
D RKNN: [11:24:25.873] 9   ConvRelu    model.2.cv3.conv.bias        INT32     (64)           | 0x00006cc0 0x00006ec0 0x00000200
D RKNN: [11:24:25.873] 10  ConvRelu    model.3.conv.weight          INT8      (128,64,3,3)   | 0x000098c0 0x0001b8c0 0x00012000
D RKNN: [11:24:25.873] 10  ConvRelu    model.3.conv.bias            INT32     (128)          | 0x0001b8c0 0x0001bcc0 0x00000400
D RKNN: [11:24:25.873] 11  ConvRelu    model.4.cv1.conv.weight      INT8      (64,128,1,1)   | 0x0001bcc0 0x0001dcc0 0x00002000
D RKNN: [11:24:25.873] 11  ConvRelu    model.4.cv1.conv.bias        INT32     (64)           | 0x0001dcc0 0x0001dec0 0x00000200
D RKNN: [11:24:25.873] 12  ConvRelu    model.4.cv2.conv.weight      INT8      (64,128,1,1)   | 0x0001dec0 0x0001fec0 0x00002000
D RKNN: [11:24:25.873] 12  ConvRelu    model.4.cv2.conv.bias        INT32     (64)           | 0x0001fec0 0x000200c0 0x00000200
D RKNN: [11:24:25.873] 13  ConvRelu    model.4.m.0.cv1.conv.weight  INT8      (64,64,1,1)    | 0x000244c0 0x000254c0 0x00001000
D RKNN: [11:24:25.873] 13  ConvRelu    model.4.m.0.cv1.conv.bias    INT32     (64)           | 0x000254c0 0x000256c0 0x00000200
D RKNN: [11:24:25.873] 14  ConvReluAdd model.4.m.0.cv2.conv.weight  INT8      (64,64,3,3)    | 0x000256c0 0x0002e6c0 0x00009000
D RKNN: [11:24:25.873] 14  ConvReluAdd model.4.m.0.cv2.conv.bias    INT32     (64)           | 0x0002e6c0 0x0002e8c0 0x00000200
D RKNN: [11:24:25.873] 15  ConvRelu    model.4.m.1.cv1.conv.weight  INT8      (64,64,1,1)    | 0x0002e8c0 0x0002f8c0 0x00001000
D RKNN: [11:24:25.873] 15  ConvRelu    model.4.m.1.cv1.conv.bias    INT32     (64)           | 0x0002f8c0 0x0002fac0 0x00000200
D RKNN: [11:24:25.873] 16  ConvReluAdd model.4.m.1.cv2.conv.weight  INT8      (64,64,3,3)    | 0x0002fac0 0x00038ac0 0x00009000
D RKNN: [11:24:25.873] 16  ConvReluAdd model.4.m.1.cv2.conv.bias    INT32     (64)           | 0x00038ac0 0x00038cc0 0x00000200
D RKNN: [11:24:25.873] 18  ConvRelu    model.4.cv3.conv.weight      INT8      (128,128,1,1)  | 0x000200c0 0x000240c0 0x00004000
D RKNN: [11:24:25.873] 18  ConvRelu    model.4.cv3.conv.bias        INT32     (128)          | 0x000240c0 0x000244c0 0x00000400
D RKNN: [11:24:25.873] 19  ConvRelu    model.5.conv.weight          INT8      (256,128,3,3)  | 0x00038cc0 0x00080cc0 0x00048000
D RKNN: [11:24:25.873] 19  ConvRelu    model.5.conv.bias            INT32     (256)          | 0x00080cc0 0x000814c0 0x00000800
D RKNN: [11:24:25.873] 20  ConvRelu    model.6.cv1.conv.weight      INT8      (128,256,1,1)  | 0x000814c0 0x000894c0 0x00008000
D RKNN: [11:24:25.873] 20  ConvRelu    model.6.cv1.conv.bias        INT32     (128)          | 0x000894c0 0x000898c0 0x00000400
D RKNN: [11:24:25.873] 21  ConvRelu    model.6.cv2.conv.weight      INT8      (128,256,1,1)  | 0x000898c0 0x000918c0 0x00008000
D RKNN: [11:24:25.873] 21  ConvRelu    model.6.cv2.conv.bias        INT32     (128)          | 0x000918c0 0x00091cc0 0x00000400
D RKNN: [11:24:25.873] 22  ConvRelu    model.6.m.0.cv1.conv.weight  INT8      (128,128,1,1)  | 0x000a24c0 0x000a64c0 0x00004000
D RKNN: [11:24:25.873] 22  ConvRelu    model.6.m.0.cv1.conv.bias    INT32     (128)          | 0x000a64c0 0x000a68c0 0x00000400
D RKNN: [11:24:25.873] 23  ConvReluAdd model.6.m.0.cv2.conv.weight  INT8      (128,128,3,3)  | 0x000a68c0 0x000ca8c0 0x00024000
D RKNN: [11:24:25.873] 23  ConvReluAdd model.6.m.0.cv2.conv.bias    INT32     (128)          | 0x000ca8c0 0x000cacc0 0x00000400
D RKNN: [11:24:25.873] 24  ConvRelu    model.6.m.1.cv1.conv.weight  INT8      (128,128,1,1)  | 0x000cacc0 0x000cecc0 0x00004000
D RKNN: [11:24:25.873] 24  ConvRelu    model.6.m.1.cv1.conv.bias    INT32     (128)          | 0x000cecc0 0x000cf0c0 0x00000400
D RKNN: [11:24:25.873] 25  ConvReluAdd model.6.m.1.cv2.conv.weight  INT8      (128,128,3,3)  | 0x000cf0c0 0x000f30c0 0x00024000
D RKNN: [11:24:25.873] 25  ConvReluAdd model.6.m.1.cv2.conv.bias    INT32     (128)          | 0x000f30c0 0x000f34c0 0x00000400
D RKNN: [11:24:25.873] 26  ConvRelu    model.6.m.2.cv1.conv.weight  INT8      (128,128,1,1)  | 0x000f34c0 0x000f74c0 0x00004000
D RKNN: [11:24:25.873] 26  ConvRelu    model.6.m.2.cv1.conv.bias    INT32     (128)          | 0x000f74c0 0x000f78c0 0x00000400
D RKNN: [11:24:25.873] 27  ConvReluAdd model.6.m.2.cv2.conv.weight  INT8      (128,128,3,3)  | 0x000f78c0 0x0011b8c0 0x00024000
D RKNN: [11:24:25.873] 27  ConvReluAdd model.6.m.2.cv2.conv.bias    INT32     (128)          | 0x0011b8c0 0x0011bcc0 0x00000400
D RKNN: [11:24:25.873] 29  ConvRelu    model.6.cv3.conv.weight      INT8      (256,256,1,1)  | 0x00091cc0 0x000a1cc0 0x00010000
D RKNN: [11:24:25.873] 29  ConvRelu    model.6.cv3.conv.bias        INT32     (256)          | 0x000a1cc0 0x000a24c0 0x00000800
D RKNN: [11:24:25.873] 30  ConvRelu    model.7.conv.weight          INT8      (512,256,3,3)  | 0x0011bcc0 0x0023bcc0 0x00120000
D RKNN: [11:24:25.873] 30  ConvRelu    model.7.conv.bias            INT32     (512)          | 0x0023bcc0 0x0023ccc0 0x00001000
D RKNN: [11:24:25.873] 31  ConvRelu    model.8.cv1.conv.weight      INT8      (256,512,1,1)  | 0x0023ccc0 0x0025ccc0 0x00020000
D RKNN: [11:24:25.873] 31  ConvRelu    model.8.cv1.conv.bias        INT32     (256)          | 0x0025ccc0 0x0025d4c0 0x00000800
D RKNN: [11:24:25.873] 32  ConvRelu    model.8.cv2.conv.weight      INT8      (256,512,1,1)  | 0x0025d4c0 0x0027d4c0 0x00020000
D RKNN: [11:24:25.873] 32  ConvRelu    model.8.cv2.conv.bias        INT32     (256)          | 0x0027d4c0 0x0027dcc0 0x00000800
D RKNN: [11:24:25.873] 33  ConvRelu    model.8.m.0.cv1.conv.weight  INT8      (256,256,1,1)  | 0x002becc0 0x002cecc0 0x00010000
D RKNN: [11:24:25.873] 33  ConvRelu    model.8.m.0.cv1.conv.bias    INT32     (256)          | 0x002cecc0 0x002cf4c0 0x00000800
D RKNN: [11:24:25.873] 34  ConvReluAdd model.8.m.0.cv2.conv.weight  INT8      (256,256,3,3)  | 0x002cf4c0 0x0035f4c0 0x00090000
D RKNN: [11:24:25.873] 34  ConvReluAdd model.8.m.0.cv2.conv.bias    INT32     (256)          | 0x0035f4c0 0x0035fcc0 0x00000800
D RKNN: [11:24:25.873] 36  ConvRelu    model.8.cv3.conv.weight      INT8      (512,512,1,1)  | 0x0027dcc0 0x002bdcc0 0x00040000
D RKNN: [11:24:25.873] 36  ConvRelu    model.8.cv3.conv.bias        INT32     (512)          | 0x002bdcc0 0x002becc0 0x00001000
D RKNN: [11:24:25.873] 37  ConvRelu    model.9.cv1.conv.weight      INT8      (256,512,1,1)  | 0x0035fcc0 0x0037fcc0 0x00020000
D RKNN: [11:24:25.873] 37  ConvRelu    model.9.cv1.conv.bias        INT32     (256)          | 0x0037fcc0 0x003804c0 0x00000800
D RKNN: [11:24:25.873] 45  ConvRelu    model.9.cv2.conv.weight      INT8      (512,1024,1,1) | 0x003804c0 0x004004c0 0x00080000
D RKNN: [11:24:25.873] 45  ConvRelu    model.9.cv2.conv.bias        INT32     (512)          | 0x004004c0 0x004014c0 0x00001000
D RKNN: [11:24:25.873] 46  ConvRelu    model.10.conv.weight         INT8      (256,512,1,1)  | 0x004014c0 0x004214c0 0x00020000
D RKNN: [11:24:25.873] 46  ConvRelu    model.10.conv.bias           INT32     (256)          | 0x004214c0 0x00421cc0 0x00000800
D RKNN: [11:24:25.873] 47  Resize      219                          FLOAT     (0)            | 0x00000000 0x00000000 0x00000000
D RKNN: [11:24:25.873] 47  Resize      289                          FLOAT     (4)            | 0x006f6040 0x006f60c0 0x00000080
D RKNN: [11:24:25.873] 49  ConvRelu    model.13.cv1.conv.weight     INT8      (128,512,1,1)  | 0x00421cc0 0x00431cc0 0x00010000
D RKNN: [11:24:25.873] 49  ConvRelu    model.13.cv1.conv.bias       INT32     (128)          | 0x00431cc0 0x004320c0 0x00000400
D RKNN: [11:24:25.873] 50  ConvRelu    model.13.cv2.conv.weight     INT8      (128,512,1,1)  | 0x004320c0 0x004420c0 0x00010000
D RKNN: [11:24:25.873] 50  ConvRelu    model.13.cv2.conv.bias       INT32     (128)          | 0x004420c0 0x004424c0 0x00000400
D RKNN: [11:24:25.873] 51  ConvRelu    model.13.m.0.cv1.conv.weight INT8      (128,128,1,1)  | 0x00452cc0 0x00456cc0 0x00004000
D RKNN: [11:24:25.873] 51  ConvRelu    model.13.m.0.cv1.conv.bias   INT32     (128)          | 0x00456cc0 0x004570c0 0x00000400
D RKNN: [11:24:25.873] 52  ConvRelu    model.13.m.0.cv2.conv.weight INT8      (128,128,3,3)  | 0x004570c0 0x0047b0c0 0x00024000
D RKNN: [11:24:25.873] 52  ConvRelu    model.13.m.0.cv2.conv.bias   INT32     (128)          | 0x0047b0c0 0x0047b4c0 0x00000400
D RKNN: [11:24:25.873] 54  ConvRelu    model.13.cv3.conv.weight     INT8      (256,256,1,1)  | 0x004424c0 0x004524c0 0x00010000
D RKNN: [11:24:25.873] 54  ConvRelu    model.13.cv3.conv.bias       INT32     (256)          | 0x004524c0 0x00452cc0 0x00000800
D RKNN: [11:24:25.873] 55  ConvRelu    model.14.conv.weight         INT8      (128,256,1,1)  | 0x0047b4c0 0x004834c0 0x00008000
D RKNN: [11:24:25.873] 55  ConvRelu    model.14.conv.bias           INT32     (128)          | 0x004834c0 0x004838c0 0x00000400
D RKNN: [11:24:25.873] 56  Resize      238                          FLOAT     (0)            | 0x00000000 0x00000000 0x00000000
D RKNN: [11:24:25.873] 56  Resize      290                          FLOAT     (4)            | 0x006f60c0 0x006f6140 0x00000080
D RKNN: [11:24:25.873] 58  ConvRelu    model.17.cv1.conv.weight     INT8      (64,256,1,1)   | 0x004838c0 0x004878c0 0x00004000
D RKNN: [11:24:25.873] 58  ConvRelu    model.17.cv1.conv.bias       INT32     (64)           | 0x004878c0 0x00487ac0 0x00000200
D RKNN: [11:24:25.873] 59  ConvRelu    model.17.cv2.conv.weight     INT8      (64,256,1,1)   | 0x00487ac0 0x0048bac0 0x00004000
D RKNN: [11:24:25.873] 59  ConvRelu    model.17.cv2.conv.bias       INT32     (64)           | 0x0048bac0 0x0048bcc0 0x00000200
D RKNN: [11:24:25.873] 60  ConvRelu    model.17.m.0.cv1.conv.weight INT8      (64,64,1,1)    | 0x004900c0 0x004910c0 0x00001000
D RKNN: [11:24:25.873] 60  ConvRelu    model.17.m.0.cv1.conv.bias   INT32     (64)           | 0x004910c0 0x004912c0 0x00000200
D RKNN: [11:24:25.873] 61  ConvRelu    model.17.m.0.cv2.conv.weight INT8      (64,64,3,3)    | 0x004912c0 0x0049a2c0 0x00009000
D RKNN: [11:24:25.873] 61  ConvRelu    model.17.m.0.cv2.conv.bias   INT32     (64)           | 0x0049a2c0 0x0049a4c0 0x00000200
D RKNN: [11:24:25.873] 63  ConvRelu    model.17.cv3.conv.weight     INT8      (128,128,1,1)  | 0x0048bcc0 0x0048fcc0 0x00004000
D RKNN: [11:24:25.873] 63  ConvRelu    model.17.cv3.conv.bias       INT32     (128)          | 0x0048fcc0 0x004900c0 0x00000400
D RKNN: [11:24:25.873] 64  ConvRelu    model.18.conv.weight         INT8      (128,128,3,3)  | 0x0049a4c0 0x004be4c0 0x00024000
D RKNN: [11:24:25.873] 64  ConvRelu    model.18.conv.bias           INT32     (128)          | 0x004be4c0 0x004be8c0 0x00000400
D RKNN: [11:24:25.873] 66  ConvSigmoid model.24.m.0.weight          INT8      (255,128,1,1)  | 0x006bb8c0 0x006c3840 0x00007f80
D RKNN: [11:24:25.873] 66  ConvSigmoid model.24.m.0.bias            INT32     (255)          | 0x006c3840 0x006c4040 0x00000800
D RKNN: [11:24:25.873] 67  ConvRelu    model.20.cv1.conv.weight     INT8      (128,256,1,1)  | 0x004be8c0 0x004c68c0 0x00008000
D RKNN: [11:24:25.873] 67  ConvRelu    model.20.cv1.conv.bias       INT32     (128)          | 0x004c68c0 0x004c6cc0 0x00000400
D RKNN: [11:24:25.873] 68  ConvRelu    model.20.cv2.conv.weight     INT8      (128,256,1,1)  | 0x004c6cc0 0x004cecc0 0x00008000
D RKNN: [11:24:25.873] 68  ConvRelu    model.20.cv2.conv.bias       INT32     (128)          | 0x004cecc0 0x004cf0c0 0x00000400
D RKNN: [11:24:25.873] 69  ConvRelu    model.20.m.0.cv1.conv.weight INT8      (128,128,1,1)  | 0x004df8c0 0x004e38c0 0x00004000
D RKNN: [11:24:25.873] 69  ConvRelu    model.20.m.0.cv1.conv.bias   INT32     (128)          | 0x004e38c0 0x004e3cc0 0x00000400
D RKNN: [11:24:25.873] 70  ConvRelu    model.20.m.0.cv2.conv.weight INT8      (128,128,3,3)  | 0x004e3cc0 0x00507cc0 0x00024000
D RKNN: [11:24:25.873] 70  ConvRelu    model.20.m.0.cv2.conv.bias   INT32     (128)          | 0x00507cc0 0x005080c0 0x00000400
D RKNN: [11:24:25.873] 72  ConvRelu    model.20.cv3.conv.weight     INT8      (256,256,1,1)  | 0x004cf0c0 0x004df0c0 0x00010000
D RKNN: [11:24:25.873] 72  ConvRelu    model.20.cv3.conv.bias       INT32     (256)          | 0x004df0c0 0x004df8c0 0x00000800
D RKNN: [11:24:25.873] 73  ConvRelu    model.21.conv.weight         INT8      (256,256,3,3)  | 0x005080c0 0x005980c0 0x00090000
D RKNN: [11:24:25.873] 73  ConvRelu    model.21.conv.bias           INT32     (256)          | 0x005980c0 0x005988c0 0x00000800
D RKNN: [11:24:25.873] 75  ConvSigmoid model.24.m.1.weight          INT8      (255,256,1,1)  | 0x006c4040 0x006d3f40 0x0000ff00
D RKNN: [11:24:25.873] 75  ConvSigmoid model.24.m.1.bias            INT32     (255)          | 0x006d3f40 0x006d4740 0x00000800
D RKNN: [11:24:25.873] 76  ConvRelu    model.23.cv1.conv.weight     INT8      (256,512,1,1)  | 0x005988c0 0x005b88c0 0x00020000
D RKNN: [11:24:25.873] 76  ConvRelu    model.23.cv1.conv.bias       INT32     (256)          | 0x005b88c0 0x005b90c0 0x00000800
D RKNN: [11:24:25.873] 77  ConvRelu    model.23.cv2.conv.weight     INT8      (256,512,1,1)  | 0x005b90c0 0x005d90c0 0x00020000
D RKNN: [11:24:25.873] 77  ConvRelu    model.23.cv2.conv.bias       INT32     (256)          | 0x005d90c0 0x005d98c0 0x00000800
D RKNN: [11:24:25.873] 78  ConvRelu    model.23.m.0.cv1.conv.weight INT8      (256,256,1,1)  | 0x0061a8c0 0x0062a8c0 0x00010000
D RKNN: [11:24:25.873] 78  ConvRelu    model.23.m.0.cv1.conv.bias   INT32     (256)          | 0x0062a8c0 0x0062b0c0 0x00000800
D RKNN: [11:24:25.873] 79  ConvRelu    model.23.m.0.cv2.conv.weight INT8      (256,256,3,3)  | 0x0062b0c0 0x006bb0c0 0x00090000
D RKNN: [11:24:25.873] 79  ConvRelu    model.23.m.0.cv2.conv.bias   INT32     (256)          | 0x006bb0c0 0x006bb8c0 0x00000800
D RKNN: [11:24:25.873] 81  ConvRelu    model.23.cv3.conv.weight     INT8      (512,512,1,1)  | 0x005d98c0 0x006198c0 0x00040000
D RKNN: [11:24:25.873] 81  ConvRelu    model.23.cv3.conv.bias       INT32     (512)          | 0x006198c0 0x0061a8c0 0x00001000
D RKNN: [11:24:25.873] 82  ConvSigmoid model.24.m.2.weight          INT8      (255,512,1,1)  | 0x006d4740 0x006f4540 0x0001fe00
D RKNN: [11:24:25.873] 82  ConvSigmoid model.24.m.2.bias            INT32     (255)          | 0x006f4540 0x006f4d40 0x00000800
D RKNN: [11:24:25.873] ----------------------------------------------------------------------+---------------------------------
D RKNN: [11:24:25.878] ----------------------------------------
D RKNN: [11:24:25.879] Total Internal Memory Size: 6693.75KB
D RKNN: [11:24:25.879] Total Weight Memory Size: 7128.44KB
D RKNN: [11:24:25.879] ----------------------------------------
D RKNN: [11:24:25.879] <<<<<<<< end: rknn::RKNNMemStatisticsPass
I rknn buiding done.
done
--> Export rknn model
done
--> Init runtime environment
I target set by user is: rv1106
I Check RV1106 board npu runtime version
I Starting ntp or adb, target is RV1106
I Device [bd547ee6900c058b] not found in ntb device list.
I Start adb...
I Connect to Device success!
I NPUTransfer: Starting NPU Transfer Client, Transfer version 2.1.0 (b5861e7@2020-11-23T11:50:36)
D NPUTransfer: Transfer spec = local:transfer_proxy
D NPUTransfer: Transfer interface successfully opened, fd = 3
D RKNNAPI: ==============================================
D RKNNAPI: RKNN VERSION:
D RKNNAPI:   API: 1.6.0 (535b468 build@2023-12-11T09:05:46)
D RKNNAPI:   DRV: rknn_server: 1.6.0 (535b468 build@2023-12-11T17:05:28)
D RKNNAPI:   DRV: rknnrt: 1.6.0 (9a7b5d24c@2023-12-13T17:33:10)
D RKNNAPI: ==============================================
D RKNNAPI: Input tensors:
D RKNNAPI:   index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=1228800, w_stride = 0, size_with_stride = 0, fmt=NHWC, type=UINT8, qnt_type=AFFINE, zp=-128, scale=0.003922
D RKNNAPI: Output tensors:
D RKNNAPI:   index=0, name=output, n_dims=4, dims=[1, 255, 80, 80], n_elems=1632000, size=1632000, w_stride = 0, size_with_stride = 0, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003860
D RKNNAPI:   index=1, name=283, n_dims=4, dims=[1, 255, 40, 40], n_elems=408000, size=408000, w_stride = 0, size_with_stride = 0, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003922
D RKNNAPI:   index=2, name=285, n_dims=4, dims=[1, 255, 20, 20], n_elems=102000, size=102000, w_stride = 0, size_with_stride = 0, fmt=NCHW, type=INT8, qnt_type=AFFINE, zp=-128, scale=0.003915
done
--> Running model
doneclass        score      xmin, ymin, xmax, ymax
--------------------------------------------------person       0.884     [ 209,  244,  286,  506]person       0.868     [ 478,  238,  559,  526]person       0.825     [ 110,  238,  230,  534]person       0.339     [  79,  354,  122,  516]bus         0.705     [  94,  129,  553,  468]
Save results to result.jpg!
D NPUTransfer: Transfer client closed, fd = 3

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