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products:sbc:vim3:npu:ksnn:ksnn-usage

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products:sbc:vim3:npu:ksnn:ksnn-usage [2023/12/21 01:29]
louis 		products:sbc:vim3:npu:ksnn:ksnn-usage [2024/04/15 03:21] (current)
louis
Line 16: Line 16:
 ```shell ```shell
 $ git clone --recursive https://github.com/khadas/ksnn.git $ git clone --recursive https://github.com/khadas/ksnn.git
-``` 
- 
-Install the dependencies: 
- 
-```shell 
-$ pip3 install matplotlib 
 ``` ```
  
Line 28: Line 22:
 ```shell ```shell
 $ cd ksnn/ksnn $ cd ksnn/ksnn
-$ pip3 install ksnn-1.3-py3-none-any.whl+$ pip3 install ksnn-1.4-py3-none-any.whl
 ``` ```
  
Line 40: Line 34:
 ``` ```
  
-Choose ''tensorflow'' and ''inceptionv3.py'' for example, other demos are similar.+Choose ''keras'' and ''xception.py'' for example, other demos are similar.
  
 ```shell ```shell
-$ cd tensorflow && ls -1 +$ cd keras && ls -1 
-data  +data
 libs libs
 models models
-README.md   +README.md 
-box_priors.txt +xception.py
-inceptionv3.py +
-mobilenet_ssd_picture.py+
 ``` ```
  
Line 56: Line 48:
  
 ```shell ```shell
-$ ~/ksnn/examples/tensorflow$ cat README.md+$ ~/ksnn/examples/keras$ cat README.md
  
-# run+# Run 
 + 
 +$ python3 xception.py --model ./models/VIM3/xception_uint8.nb --library ./libs/libnn_xception_uint8.so --picture data/goldfish_299x299.jpg --level 0
  
-$ python3 inceptionv3.py --model ./models/VIM3/inceptionv3.nb --library ./libs/libnn_inceptionv3.so --picture ./data/goldfish_299x299.jpg --level 0 
-$ python3 mobilenet_ssd_picture.py --model ./models/VIM3/mobilenet_ssd.nb --library ./libs/libnn_mobilenet_ssd.so --picture data/1080p.bmp --level 0 
  
 # Convert # Convert
  
 +# uint8
 $ ./convert \ $ ./convert \
---model-name inception \ +--model-name xception \ 
---platform tensorflow \ +--platform keras \ 
---model inception_v3_2016_08_28_frozen.pb \ +--model /home/yan/yan/Yan/models-zoo/keras/xception/xception.h5 \ 
---input-size-list '299,299,3' \ +--mean-values '127.5 127.5 127.5 0.007843137' \
---inputs input \ +
---outputs InceptionV3/Predictions/Reshape_1 \ +
---mean-values '128 128 128 0.0078125' \ +
---quantized-dtype asymmetric_affine \ +
---kboard VIM3 --print-level 1 +
- +
-$ ./convert \ +
---model-name mobilenet_ssd \ +
---platform tensorflow \ +
---model ssd_mobilenet_v1_coco_2017_11_17.pb \ +
---input-size-list '300,300,3' \ +
---inputs FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/BatchNorm/batchnorm/mul_1 \ +
---outputs "'concat concat_1'" \ +
---mean-values '127.5 127.5 127.5 0.0078431' \+
 --quantized-dtype asymmetric_affine \ --quantized-dtype asymmetric_affine \
 +--source-files ./data/dataset/dataset0.txt \
 --kboard VIM3 --print-level 1 --kboard VIM3 --print-level 1
  
Line 91: Line 71:
 ``` ```
  
-Run ''inceptionv3.py'':+Run ''xception.py'':
  
 ```shell ```shell
-$ python3 inceptionv3.py --model ./models/VIM3/inceptionv3.nb --library ./libs/libnn_inceptionv3.so --picture ./data/goldfish_299x299.jpg --level 0 +$ python3 xception.py --model ./models/VIM3/xception_uint8.nb --library ./libs/libnn_xception_uint8.so --picture data/goldfish_299x299.jpg --level 0 
- |--- KSNN Version: v1.3 +---| + |---+ KSNN Version: v1.4 +---| 
 Start init neural network ... Start init neural network ...
 Done. Done.
 Get input data ... Get input data ...
-Done.+Done
 Start inference ... Start inference ...
-Done. inference :  0.042353153228759766 +Done. inference time:  0.07830595970153809 
------ Show Top5 +----- +----Xception---- 
-     2: 0.93457 +-----TOP 5----- 
-   795: 0.00328 +[1]: 0.99609375 
-   408: 0.00158 +[0]: 0.0009250640869140625 
-   974: 0.00148 +[391]: 0.00019299983978271484 
-   393: 0.00093+[29]: 0.00017976760864257812 
 +[124]: 0.00016736984252929688
 ``` ```
  
Line 113: Line 94:
  
 ```shell ```shell
-$ python3 inceptionv3.py --model ./models/VIM3/inceptionv3.nb --library ./libs/libnn_inceptionv3.so --picture ./data/goldfish_299x299.jpg --level 2 +$ python3 xception.py --model ./models/VIM3/xception_uint8.nb --library ./libs/libnn_xception_uint8.so --picture data/goldfish_299x299.jpg --level 2 
- |--- KSNN Version: v1.3 +---| + |---+ KSNN Version: v1.4 +---| 
 Start init neural network ... Start init neural network ...
 #productname=VIPNano-QI, pid=0x88 #productname=VIPNano-QI, pid=0x88
-Create Neural Network: 283ms or 283181us+Create Neural Network: 47ms or 47458us
 Done. Done.
 Get input data ... Get input data ...
-Done.+Done
 Start inference ... Start inference ...
 Start run graph [1] times... Start run graph [1] times...
Line 126: Line 107:
 current device id=0, AXI SRAM base address=0xff000000 current device id=0, AXI SRAM base address=0xff000000
 ---------------------------Begin VerifyTiling ------------------------- ---------------------------Begin VerifyTiling -------------------------
-AXI-SRAM = 1048576 Bytes VIP-SRAM = 522240 Bytes SWTILING_PHASE_FEATURES[1, 1, 0]+AXI-SRAM = 1048320 Bytes VIP-SRAM = 522240 Bytes SWTILING_PHASE_FEATURES[1, 1, 0]
   0 NBG [(   0    0    0 0,        0, 0x(nil)(0x(nil), 0x(nil)) ->    0    0    0 0,        0, 0x(nil)(0x(nil), 0x(nil))) k(0 0    0,        0) pad(0 0) pool(0 0, 0 0)]   0 NBG [(   0    0    0 0,        0, 0x(nil)(0x(nil), 0x(nil)) ->    0    0    0 0,        0, 0x(nil)(0x(nil), 0x(nil))) k(0 0    0,        0) pad(0 0) pool(0 0, 0 0)]
  
- id IN [ x  y  w   h ]   OUT  [ x  y  w  h ] (tx, ty, kpc) (ic, kc, kc/ks, ks/eks, kernel_type) + id IN [ x  y  w   h ]   OUT  [ x  y  w  h ] (tx, ty, kpc) (ic, kc, kc/ks, ks/eks, kernel_type) NNT(in, out)
-   0 NBG DD 0x(nil) [   0    0        0        0] -> DD 0x(nil) [   0    0        0        0] (  0,   0,   0) (       0,        0, 0.000000%, 0.000000%, NONE)+
  
-PreLoadWeightBiases = 1048576  100.000000%+ id | opid IN [ x  y  w   h ]   OUT  [ x  y  w  h ] (tx, ty, kpc) (ic, kc, kc/ks, ks/eks, kernel_type) NNT(in, out) 
 +  0 |   0 NBG DD 0x00000000 [   0    0        0        0] -> DD 0x00000000 [   0    0        0        0] (  0,   0,   0) (       0,        0, 0.000000%, 0.000000%, NONE) (       0,        0) 
 + 
 +PreLoadWeightBiases = 1048320  100.000000%
 ---------------------------End VerifyTiling ------------------------- ---------------------------End VerifyTiling -------------------------
 layer_id: 0 layer name:network_binary_graph operation[0]:unkown operation type target:unkown operation target. layer_id: 0 layer name:network_binary_graph operation[0]:unkown operation type target:unkown operation target.
 uid: 0 uid: 0
 abs_op_id: 0 abs_op_id: 0
-execution time:             20552 us +execution time:             97432 us 
-[     1] TOTAL_READ_BANDWIDTH  (MByte): 67.540481 +[     1] TOTAL_READ_BANDWIDTH  (MByte): 135.867976 
-[     2] TOTAL_WRITE_BANDWIDTH (MByte): 18.245340 +[     2] TOTAL_WRITE_BANDWIDTH (MByte): 74.167511 
-[     3] AXI_READ_BANDWIDTH  (MByte): 30.711348 +[     3] AXI_READ_BANDWIDTH  (MByte): 61.521023 
-[     4] AXI_WRITE_BANDWIDTH (MByte): 15.229973 +[     4] AXI_WRITE_BANDWIDTH (MByte): 50.982863 
-[     5] DDR_READ_BANDWIDTH (MByte): 36.829133 +[     5] DDR_READ_BANDWIDTH (MByte): 74.346954 
-[     6] DDR_WRITE_BANDWIDTH (MByte): 3.015367 +[     6] DDR_WRITE_BANDWIDTH (MByte): 23.184648 
-[     7] GPUTOTALCYCLES: 94344921 +[     7] GPUTOTALCYCLES: 77303691 
-[     8] GPUIDLECYCLES: 78109663 +[     8] GPUIDLECYCLES: 22877687 
-VPC_ELAPSETIME: 118090+VPC_ELAPSETIME: 97775
 ********* *********
-Run the 1 time: 118.00ms or 118636.00us+Run the 1 time: 100.00ms or 100247.00us
 vxProcessGraph execution time: vxProcessGraph execution time:
-Total   118.00ms or 118996.00us +Total   100.00ms or 100303.00us 
-Average 119.00ms or 118996.00us +Average 100.30ms or 100303.00us 
-Done. inference :  0.1422710418701172 +Done. inference time:  0.11749053001403809 
------ Show Top5 +----- +----Xception---- 
-     2: 0.93457 +-----TOP 5----- 
-   795: 0.00328 +[1]: 0.99609375 
-   408: 0.00158 +[0]: 0.0009250640869140625 
-   974: 0.00148 +[391]: 0.00019299983978271484 
-   393: 0.00093+[29]: 0.00017976760864257812 
 +[124]: 0.00016736984252929688
 ``` ```
  
Line 170: Line 154:
 cd ksnn/examples/darknet cd ksnn/examples/darknet
  
-python3 hand-cap.py --model ./models/VIM3/hand.nb \ +python3 yolov3-cap.py --model ./models/VIM3/yolov3_uint8.nb --library ./libs/libnn_yolov3_uint8.so --device X
-  --library ./libs/libnn_hand.so --device X +
-``` +
- +
-2. Currently, the only demo that supports RTSP is the yolo series. Take Yolov3 as an example, +
- +
-```sh +
-cd ksnn/examples/darknet +
- +
-python3 flask-yolov3.py --model ./models/VIM3/yolov3.nb \ +
-  --library ./libs/libnn_yolov3.so --device X+
 ``` ```
  
Last modified: 2024/04/15 03:21 by louis

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