'프로그램 사용'에 해당되는 글 2263건

  1. 2021.01.19 cmake 빌드 에러시
  2. 2021.01.19 g++ 은 정적 빌드가 안되나?
  3. 2021.01.18 tensorflow-lite 모델
  4. 2021.01.18 tensorflow-lite minimal.cc 실행
  5. 2021.01.16 Blaze Video / FM 듣기
  6. 2021.01.14 mobileNET/SSD
  7. 2021.01.14 caffe
  8. 2021.01.11 git error: RPC failed; curl ... remote end hung up
  9. 2021.01.11 darknet detect
  10. 2021.01.10 CNN convolution과 maxpool
프로그램 사용/make, configure2021. 1. 19. 17:29

cmake 빌드 에러시

어우.. cmake 너무 시르다..

빌드 하는데 안되면 CMakeCache.txt 파일 삭제하고 재시도 하면 된다.

 

[링크 : https://stackoverflow.com/questions/45518317/in-source-builds-are-not-allowed-in-cmake]

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Posted by 구차니
프로그램 사용/gcc2021. 1. 19. 11:00

g++ 은 정적 빌드가 안되나?

 

tensorflow lite 빌드해서 static libaray 로 libtensorflow-lite.a는 얻어냈는데

얘를 링크해서 빌드하니 dynamic link가 된다.

 

궁금해서 hello.cpp 이런거 하나 구해서 g++로 빌드하니

무조건 끌어가는 것 같긴한데

$ cat hello.cpp
// hello.cpp file
#include <iostream>
int main()
{
    std::cout << "Hello Geek\n";
    return 0;
}

$ g++ hello.cpp
$ ldd a.out
        linux-vdso.so.1 (0x7efa4000)
        /usr/lib/arm-linux-gnueabihf/libarmmem-${PLATFORM}.so => /usr/lib/arm-linux-gnueabihf/libarmmem-v7l.so (0x76f7e000)
        libstdc++.so.6 => /usr/lib/arm-linux-gnueabihf/libstdc++.so.6 (0x76e19000)
        libm.so.6 => /lib/arm-linux-gnueabihf/libm.so.6 (0x76d97000)
        libgcc_s.so.1 => /lib/arm-linux-gnueabihf/libgcc_s.so.1 (0x76d6a000)
        libc.so.6 => /lib/arm-linux-gnueabihf/libc.so.6 (0x76c1c000)
        /lib/ld-linux-armhf.so.3 (0x76f93000)

 

혹시나 해서 g++ -static 옵션을 주니 되긴 되네..

$ g++ hello.cpp -o b.out -static
$ ldd b.out
        not a dynamic executable
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Posted by 구차니
프로그램 사용/yolo_tensorflow2021. 1. 18. 18:25

tensorflow-lite 모델

흐음.. classification과 object-detection의 차이를 모르겠네..

아무튼 classification 쪽에 만들어진 model이 꽤 있으니 이걸 이용하면 될 듯.

 

[링크 : https://tfhub.dev/s?deployment-format=lite&module-type=image-classification]

[링크 : https://tfhub.dev/s?deployment-format=lite&module-type=image-object-detection]

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Posted by 구차니
프로그램 사용/yolo_tensorflow2021. 1. 18. 18:20

tensorflow-lite minimal.cc 실행

+ 2021.01.19

./tensorflow/lite/tools/make/gen/rpi_armv7l/bin 에 빌드된 파일이 존재한다 -_-

왜 이 고생을 했지? ㅠㅠㅠㅠ

./tensorflow/lite/tools/make/build_rpi_lib.sh clean # clean object files
./tensorflow/lite/tools/make/build_rpi_lib.sh -j 16 # run with 16 jobs to leverage more CPU cores
./tensorflow/lite/tools/make/build_rpi_lib.sh label_image # # build label_image binary

[링크 : https://www.tensorflow.org/lite/guide/build_rpi]

 

---

어찌어찌 겨우겨우 libtensorflow-lite.a 를 생성해서

minimal.cc를 빌드해서 돌려보는데 감동...?까진 아니고

아무튼 텐서플로우 lite 자체는 static 한데, minimal.cc는 static 할 수 없는건 무슨 묘미인가...

$ ldd minimal
        linux-vdso.so.1 (0x7efc0000)
        /usr/lib/arm-linux-gnueabihf/libarmmem-${PLATFORM}.so => /usr/lib/arm-linux-gnueabihf/libarmmem-v7l.so (0x76eef000)
        libpthread.so.0 => /lib/arm-linux-gnueabihf/libpthread.so.0 (0x76ea4000)
        libdl.so.2 => /lib/arm-linux-gnueabihf/libdl.so.2 (0x76e91000)
        libstdc++.so.6 => /lib/arm-linux-gnueabihf/libstdc++.so.6 (0x76d4a000)
        libm.so.6 => /lib/arm-linux-gnueabihf/libm.so.6 (0x76cc8000)
        libgcc_s.so.1 => /lib/arm-linux-gnueabihf/libgcc_s.so.1 (0x76c9b000)
        libc.so.6 => /lib/arm-linux-gnueabihf/libc.so.6 (0x76b4d000)
        /lib/ld-linux-armhf.so.3 (0x76f04000)

 

~/src/tensorflow_src/tensorflow/lite/examples/minimal $ ./minimal 1.tflite
=== Pre-invoke Interpreter State ===
Interpreter has 184 tensors and 64 nodes
Inputs: 175
Outputs: 167 168 169 170

Tensor   0 BoxPredictor_0/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw       4332 bytes ( 0.0 MB)  1 19 19 12
Tensor   1 BoxPredictor_0/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         48 bytes ( 0.0 MB)  12
Tensor   2 BoxPredictor_0/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       4608 bytes ( 0.0 MB)  12 1 1 384
Tensor   3 BoxPredictor_0/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw      98553 bytes ( 0.1 MB)  1 19 19 273
Tensor   4 BoxPredictor_0/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       1092 bytes ( 0.0 MB)  273
Tensor   5 BoxPredictor_0/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     104832 bytes ( 0.1 MB)  273 1 1 384
Tensor   6 BoxPredictor_0/Reshape kTfLiteUInt8  kTfLiteArenaRw       4332 bytes ( 0.0 MB)  1 1083 1 4
Tensor   7 BoxPredictor_0/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw      98553 bytes ( 0.1 MB)  1 1083 91
Tensor   8 BoxPredictor_0/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor   9 BoxPredictor_0/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  10 BoxPredictor_1/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw       2400 bytes ( 0.0 MB)  1 10 10 24
Tensor  11 BoxPredictor_1/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  12 BoxPredictor_1/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      18432 bytes ( 0.0 MB)  24 1 1 768
Tensor  13 BoxPredictor_1/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw      54600 bytes ( 0.1 MB)  1 10 10 546
Tensor  14 BoxPredictor_1/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  15 BoxPredictor_1/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     419328 bytes ( 0.4 MB)  546 1 1 768
Tensor  16 BoxPredictor_1/Reshape kTfLiteUInt8  kTfLiteArenaRw       2400 bytes ( 0.0 MB)  1 600 1 4
Tensor  17 BoxPredictor_1/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw      54600 bytes ( 0.1 MB)  1 600 91
Tensor  18 BoxPredictor_1/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  19 BoxPredictor_1/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  20 BoxPredictor_2/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw        600 bytes ( 0.0 MB)  1 5 5 24
Tensor  21 BoxPredictor_2/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  22 BoxPredictor_2/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       9216 bytes ( 0.0 MB)  24 1 1 384
Tensor  23 BoxPredictor_2/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw      13650 bytes ( 0.0 MB)  1 5 5 546
Tensor  24 BoxPredictor_2/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  25 BoxPredictor_2/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     209664 bytes ( 0.2 MB)  546 1 1 384
Tensor  26 BoxPredictor_2/Reshape kTfLiteUInt8  kTfLiteArenaRw        600 bytes ( 0.0 MB)  1 150 1 4
Tensor  27 BoxPredictor_2/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw      13650 bytes ( 0.0 MB)  1 150 91
Tensor  28 BoxPredictor_2/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  29 BoxPredictor_2/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  30 BoxPredictor_3/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw        216 bytes ( 0.0 MB)  1 3 3 24
Tensor  31 BoxPredictor_3/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  32 BoxPredictor_3/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       4608 bytes ( 0.0 MB)  24 1 1 192
Tensor  33 BoxPredictor_3/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw       4914 bytes ( 0.0 MB)  1 3 3 546
Tensor  34 BoxPredictor_3/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  35 BoxPredictor_3/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     104832 bytes ( 0.1 MB)  546 1 1 192
Tensor  36 BoxPredictor_3/Reshape kTfLiteUInt8  kTfLiteArenaRw        216 bytes ( 0.0 MB)  1 54 1 4
Tensor  37 BoxPredictor_3/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw       4914 bytes ( 0.0 MB)  1 54 91
Tensor  38 BoxPredictor_3/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  39 BoxPredictor_3/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  40 BoxPredictor_4/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw         96 bytes ( 0.0 MB)  1 2 2 24
Tensor  41 BoxPredictor_4/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  42 BoxPredictor_4/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       4608 bytes ( 0.0 MB)  24 1 1 192
Tensor  43 BoxPredictor_4/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw       2184 bytes ( 0.0 MB)  1 2 2 546
Tensor  44 BoxPredictor_4/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  45 BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     104832 bytes ( 0.1 MB)  546 1 1 192
Tensor  46 BoxPredictor_4/Reshape kTfLiteUInt8  kTfLiteArenaRw         96 bytes ( 0.0 MB)  1 24 1 4
Tensor  47 BoxPredictor_4/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw       2184 bytes ( 0.0 MB)  1 24 91
Tensor  48 BoxPredictor_4/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  49 BoxPredictor_4/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  50 BoxPredictor_5/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw         24 bytes ( 0.0 MB)  1 1 1 24
Tensor  51 BoxPredictor_5/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  52 BoxPredictor_5/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       2304 bytes ( 0.0 MB)  24 1 1 96
Tensor  53 BoxPredictor_5/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw        546 bytes ( 0.0 MB)  1 1 1 546
Tensor  54 BoxPredictor_5/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  55 BoxPredictor_5/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      52416 bytes ( 0.0 MB)  546 1 1 96
Tensor  56 BoxPredictor_5/Reshape kTfLiteUInt8  kTfLiteArenaRw         24 bytes ( 0.0 MB)  1 6 1 4
Tensor  57 BoxPredictor_5/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw        546 bytes ( 0.0 MB)  1 6 91
Tensor  58 BoxPredictor_5/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  59 BoxPredictor_5/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  60 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_192/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor  61 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_192/Relu6 kTfLiteUInt8  kTfLiteArenaRw      19200 bytes ( 0.0 MB)  1 10 10 192
Tensor  62 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_192/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  192 1 1 768
Tensor  63 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_96/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor  64 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_96/Relu6 kTfLiteUInt8  kTfLiteArenaRw       2400 bytes ( 0.0 MB)  1 5 5 96
Tensor  65 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_96/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      36864 bytes ( 0.0 MB)  96 1 1 384
Tensor  66 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_96/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor  67 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_96/Relu6 kTfLiteUInt8  kTfLiteArenaRw        864 bytes ( 0.0 MB)  1 3 3 96
Tensor  68 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_96/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      18432 bytes ( 0.0 MB)  96 1 1 192
Tensor  69 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_48/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        192 bytes ( 0.0 MB)  48
Tensor  70 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_48/Relu6 kTfLiteUInt8  kTfLiteArenaRw        192 bytes ( 0.0 MB)  1 2 2 48
Tensor  71 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_48/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       9216 bytes ( 0.0 MB)  48 1 1 192
Tensor  72 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_384/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  73 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_384/Relu6 kTfLiteUInt8  kTfLiteArenaRw       9600 bytes ( 0.0 MB)  1 5 5 384
Tensor  74 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_384/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     663552 bytes ( 0.6 MB)  384 3 3 192
Tensor  75 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_192/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor  76 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_192/Relu6 kTfLiteUInt8  kTfLiteArenaRw       1728 bytes ( 0.0 MB)  1 3 3 192
Tensor  77 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_192/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     165888 bytes ( 0.2 MB)  192 3 3 96
Tensor  78 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_192/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor  79 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_192/Relu6 kTfLiteUInt8  kTfLiteArenaRw        768 bytes ( 0.0 MB)  1 2 2 192
Tensor  80 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_192/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     165888 bytes ( 0.2 MB)  192 3 3 96
Tensor  81 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_96/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor  82 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_96/Relu6 kTfLiteUInt8  kTfLiteArenaRw         96 bytes ( 0.0 MB)  1 1 1 96
Tensor  83 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_96/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      41472 bytes ( 0.0 MB)  96 3 3 48
Tensor  84 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  85 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 150 150 24
Tensor  86 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        648 bytes ( 0.0 MB)  24 3 3 3
Tensor  87 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor  88 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  89 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor  90 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  91 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor  92 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor  93 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor  94 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  95 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor  96 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  97 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor  98 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor  99 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      38400 bytes ( 0.0 MB)  1 10 10 384
Tensor 100 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 101 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor 102 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       3072 bytes ( 0.0 MB)  768
Tensor 103 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      76800 bytes ( 0.1 MB)  1 10 10 768
Tensor 104 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     294912 bytes ( 0.3 MB)  768 1 1 384
Tensor 105 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      76800 bytes ( 0.1 MB)  1 10 10 768
Tensor 106 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       3072 bytes ( 0.0 MB)  768
Tensor 107 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       6912 bytes ( 0.0 MB)  1 3 3 768
Tensor 108 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       3072 bytes ( 0.0 MB)  768
Tensor 109 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      76800 bytes ( 0.1 MB)  1 10 10 768
Tensor 110 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     589824 bytes ( 0.6 MB)  768 1 1 768
Tensor 111 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 150 150 24
Tensor 112 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor 113 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        216 bytes ( 0.0 MB)  1 3 3 24
Tensor 114 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        192 bytes ( 0.0 MB)  48
Tensor 115 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw    1080000 bytes ( 1.0 MB)  1 150 150 48
Tensor 116 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       1152 bytes ( 0.0 MB)  48 1 1 24
Tensor 117 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     270000 bytes ( 0.3 MB)  1 75 75 48
Tensor 118 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        192 bytes ( 0.0 MB)  48
Tensor 119 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        432 bytes ( 0.0 MB)  1 3 3 48
Tensor 120 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor 121 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 75 75 96
Tensor 122 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       4608 bytes ( 0.0 MB)  96 1 1 48
Tensor 123 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 75 75 96
Tensor 124 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor 125 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        864 bytes ( 0.0 MB)  1 3 3 96
Tensor 126 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor 127 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 75 75 96
Tensor 128 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       9216 bytes ( 0.0 MB)  96 1 1 96
Tensor 129 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 38 38 96
Tensor 130 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor 131 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        864 bytes ( 0.0 MB)  1 3 3 96
Tensor 132 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor 133 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     277248 bytes ( 0.3 MB)  1 38 38 192
Tensor 134 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      18432 bytes ( 0.0 MB)  192 1 1 96
Tensor 135 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     277248 bytes ( 0.3 MB)  1 38 38 192
Tensor 136 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor 137 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       1728 bytes ( 0.0 MB)  1 3 3 192
Tensor 138 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor 139 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     277248 bytes ( 0.3 MB)  1 38 38 192
Tensor 140 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      36864 bytes ( 0.0 MB)  192 1 1 192
Tensor 141 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      69312 bytes ( 0.1 MB)  1 19 19 192
Tensor 142 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor 143 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       1728 bytes ( 0.0 MB)  1 3 3 192
Tensor 144 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 145 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 146 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      73728 bytes ( 0.1 MB)  384 1 1 192
Tensor 147 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 148 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 149 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor 150 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 151 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 152 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor 153 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 154 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 155 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor 156 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 157 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 158 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor 159 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 160 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 161 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor 162 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 163 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 164 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor 165 Squeeze              kTfLiteUInt8  kTfLiteArenaRw       7668 bytes ( 0.0 MB)  1 1917 4
Tensor 166 Squeeze_shape        kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor 167 TFLite_Detection_PostProcess kTfLiteFloat32  kTfLiteArenaRw        160 bytes ( 0.0 MB)  1 10 4
Tensor 168 TFLite_Detection_PostProcess:1 kTfLiteFloat32  kTfLiteArenaRw         40 bytes ( 0.0 MB)  1 10
Tensor 169 TFLite_Detection_PostProcess:2 kTfLiteFloat32  kTfLiteArenaRw         40 bytes ( 0.0 MB)  1 10
Tensor 170 TFLite_Detection_PostProcess:3 kTfLiteFloat32  kTfLiteArenaRw          4 bytes ( 0.0 MB)  1
Tensor 171 anchors              kTfLiteUInt8   kTfLiteMmapRo       7668 bytes ( 0.0 MB)  1917 4
Tensor 172 concat               kTfLiteUInt8  kTfLiteArenaRw       7668 bytes ( 0.0 MB)  1 1917 1 4
Tensor 173 concat_1             kTfLiteUInt8  kTfLiteArenaRw     174447 bytes ( 0.2 MB)  1 1917 91
Tensor 174 convert_scores       kTfLiteUInt8  kTfLiteArenaRw     174447 bytes ( 0.2 MB)  1 1917 91
Tensor 175 normalized_input_image_tensor kTfLiteUInt8  kTfLiteArenaRw     270000 bytes ( 0.3 MB)  1 300 300 3
Tensor 176 (null)               kTfLiteFloat32  kTfLiteArenaRw      30672 bytes ( 0.0 MB)  1917 4
Tensor 177 (null)               kTfLiteFloat32  kTfLiteArenaRw     697788 bytes ( 0.7 MB)  1917 91
Tensor 178 (null)               kTfLiteUInt8  kTfLiteArenaRw       1917 bytes ( 0.0 MB)  1917
Tensor 179 (null)               kTfLiteUInt8  kTfLiteArenaRw     607500 bytes ( 0.6 MB)  1 150 150 27
Tensor 180 (null)               kTfLiteUInt8  kTfLiteArenaRw      43200 bytes ( 0.0 MB)  1 5 5 1728
Tensor 181 (null)               kTfLiteUInt8  kTfLiteArenaRw       7776 bytes ( 0.0 MB)  1 3 3 864
Tensor 182 (null)               kTfLiteUInt8  kTfLiteArenaRw       3456 bytes ( 0.0 MB)  1 2 2 864
Tensor 183 (null)               kTfLiteUInt8  kTfLiteArenaRw        432 bytes ( 0.0 MB)  1 1 1 432

Node   0 Operator Builtin Code   3 CONV_2D
  Inputs: 175 86 84
  Outputs: 85
  Temporaries: 179
Node   1 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 85 113 112
  Outputs: 111
Node   2 Operator Builtin Code   3 CONV_2D
  Inputs: 111 116 114
  Outputs: 115
Node   3 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 115 119 118
  Outputs: 117
Node   4 Operator Builtin Code   3 CONV_2D
  Inputs: 117 122 120
  Outputs: 121
Node   5 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 121 125 124
  Outputs: 123
Node   6 Operator Builtin Code   3 CONV_2D
  Inputs: 123 128 126
  Outputs: 127
Node   7 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 127 131 130
  Outputs: 129
Node   8 Operator Builtin Code   3 CONV_2D
  Inputs: 129 134 132
  Outputs: 133
Node   9 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 133 137 136
  Outputs: 135
Node  10 Operator Builtin Code   3 CONV_2D
  Inputs: 135 140 138
  Outputs: 139
Node  11 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 139 143 142
  Outputs: 141
Node  12 Operator Builtin Code   3 CONV_2D
  Inputs: 141 146 144
  Outputs: 145
Node  13 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 145 149 148
  Outputs: 147
Node  14 Operator Builtin Code   3 CONV_2D
  Inputs: 147 152 150
  Outputs: 151
Node  15 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 151 155 154
  Outputs: 153
Node  16 Operator Builtin Code   3 CONV_2D
  Inputs: 153 158 156
  Outputs: 157
Node  17 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 157 161 160
  Outputs: 159
Node  18 Operator Builtin Code   3 CONV_2D
  Inputs: 159 164 162
  Outputs: 163
Node  19 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 163 89 88
  Outputs: 87
Node  20 Operator Builtin Code   3 CONV_2D
  Inputs: 87 92 90
  Outputs: 91
Node  21 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 91 95 94
  Outputs: 93
Node  22 Operator Builtin Code   3 CONV_2D
  Inputs: 93 98 96
  Outputs: 97
Node  23 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 97 101 100
  Outputs: 99
Node  24 Operator Builtin Code   3 CONV_2D
  Inputs: 99 104 102
  Outputs: 103
Node  25 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 103 107 106
  Outputs: 105
Node  26 Operator Builtin Code   3 CONV_2D
  Inputs: 105 110 108
  Outputs: 109
Node  27 Operator Builtin Code   3 CONV_2D
  Inputs: 109 62 60
  Outputs: 61
Node  28 Operator Builtin Code   3 CONV_2D
  Inputs: 61 74 72
  Outputs: 73
  Temporaries: 180
Node  29 Operator Builtin Code   3 CONV_2D
  Inputs: 73 65 63
  Outputs: 64
Node  30 Operator Builtin Code   3 CONV_2D
  Inputs: 64 77 75
  Outputs: 76
  Temporaries: 181
Node  31 Operator Builtin Code   3 CONV_2D
  Inputs: 76 68 66
  Outputs: 67
Node  32 Operator Builtin Code   3 CONV_2D
  Inputs: 67 80 78
  Outputs: 79
  Temporaries: 182
Node  33 Operator Builtin Code   3 CONV_2D
  Inputs: 79 71 69
  Outputs: 70
Node  34 Operator Builtin Code   3 CONV_2D
  Inputs: 70 83 81
  Outputs: 82
  Temporaries: 183
Node  35 Operator Builtin Code   3 CONV_2D
  Inputs: 97 2 1
  Outputs: 0
Node  36 Operator Builtin Code  22 RESHAPE
  Inputs: 0 8
  Outputs: 6
Node  37 Operator Builtin Code   3 CONV_2D
  Inputs: 97 5 4
  Outputs: 3
Node  38 Operator Builtin Code  22 RESHAPE
  Inputs: 3 9
  Outputs: 7
Node  39 Operator Builtin Code   3 CONV_2D
  Inputs: 109 12 11
  Outputs: 10
Node  40 Operator Builtin Code  22 RESHAPE
  Inputs: 10 18
  Outputs: 16
Node  41 Operator Builtin Code   3 CONV_2D
  Inputs: 109 15 14
  Outputs: 13
Node  42 Operator Builtin Code  22 RESHAPE
  Inputs: 13 19
  Outputs: 17
Node  43 Operator Builtin Code   3 CONV_2D
  Inputs: 73 22 21
  Outputs: 20
Node  44 Operator Builtin Code  22 RESHAPE
  Inputs: 20 28
  Outputs: 26
Node  45 Operator Builtin Code   3 CONV_2D
  Inputs: 73 25 24
  Outputs: 23
Node  46 Operator Builtin Code  22 RESHAPE
  Inputs: 23 29
  Outputs: 27
Node  47 Operator Builtin Code   3 CONV_2D
  Inputs: 76 32 31
  Outputs: 30
Node  48 Operator Builtin Code  22 RESHAPE
  Inputs: 30 38
  Outputs: 36
Node  49 Operator Builtin Code   3 CONV_2D
  Inputs: 76 35 34
  Outputs: 33
Node  50 Operator Builtin Code  22 RESHAPE
  Inputs: 33 39
  Outputs: 37
Node  51 Operator Builtin Code   3 CONV_2D
  Inputs: 79 42 41
  Outputs: 40
Node  52 Operator Builtin Code  22 RESHAPE
  Inputs: 40 48
  Outputs: 46
Node  53 Operator Builtin Code   3 CONV_2D
  Inputs: 79 45 44
  Outputs: 43
Node  54 Operator Builtin Code  22 RESHAPE
  Inputs: 43 49
  Outputs: 47
Node  55 Operator Builtin Code   3 CONV_2D
  Inputs: 82 52 51
  Outputs: 50
Node  56 Operator Builtin Code  22 RESHAPE
  Inputs: 50 58
  Outputs: 56
Node  57 Operator Builtin Code   2 CONCATENATION
  Inputs: 6 16 26 36 46 56
  Outputs: 172
Node  58 Operator Builtin Code  22 RESHAPE
  Inputs: 172 166
  Outputs: 165
Node  59 Operator Builtin Code   3 CONV_2D
  Inputs: 82 55 54
  Outputs: 53
Node  60 Operator Builtin Code  22 RESHAPE
  Inputs: 53 59
  Outputs: 57
Node  61 Operator Builtin Code   2 CONCATENATION
  Inputs: 7 17 27 37 47 57
  Outputs: 173
Node  62 Operator Builtin Code  14 LOGISTIC
  Inputs: 173
  Outputs: 174
Node  63 Operator Custom Name TFLite_Detection_PostProcess
  Inputs: 165 174 171
  Outputs: 167 168 169 170
  Temporaries: 176 177 178


=== Post-invoke Interpreter State ===
Interpreter has 184 tensors and 64 nodes
Inputs: 175
Outputs: 167 168 169 170

Tensor   0 BoxPredictor_0/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw       4332 bytes ( 0.0 MB)  1 19 19 12
Tensor   1 BoxPredictor_0/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         48 bytes ( 0.0 MB)  12
Tensor   2 BoxPredictor_0/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       4608 bytes ( 0.0 MB)  12 1 1 384
Tensor   3 BoxPredictor_0/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw      98553 bytes ( 0.1 MB)  1 19 19 273
Tensor   4 BoxPredictor_0/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       1092 bytes ( 0.0 MB)  273
Tensor   5 BoxPredictor_0/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     104832 bytes ( 0.1 MB)  273 1 1 384
Tensor   6 BoxPredictor_0/Reshape kTfLiteUInt8  kTfLiteArenaRw       4332 bytes ( 0.0 MB)  1 1083 1 4
Tensor   7 BoxPredictor_0/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw      98553 bytes ( 0.1 MB)  1 1083 91
Tensor   8 BoxPredictor_0/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor   9 BoxPredictor_0/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  10 BoxPredictor_1/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw       2400 bytes ( 0.0 MB)  1 10 10 24
Tensor  11 BoxPredictor_1/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  12 BoxPredictor_1/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      18432 bytes ( 0.0 MB)  24 1 1 768
Tensor  13 BoxPredictor_1/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw      54600 bytes ( 0.1 MB)  1 10 10 546
Tensor  14 BoxPredictor_1/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  15 BoxPredictor_1/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     419328 bytes ( 0.4 MB)  546 1 1 768
Tensor  16 BoxPredictor_1/Reshape kTfLiteUInt8  kTfLiteArenaRw       2400 bytes ( 0.0 MB)  1 600 1 4
Tensor  17 BoxPredictor_1/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw      54600 bytes ( 0.1 MB)  1 600 91
Tensor  18 BoxPredictor_1/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  19 BoxPredictor_1/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  20 BoxPredictor_2/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw        600 bytes ( 0.0 MB)  1 5 5 24
Tensor  21 BoxPredictor_2/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  22 BoxPredictor_2/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       9216 bytes ( 0.0 MB)  24 1 1 384
Tensor  23 BoxPredictor_2/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw      13650 bytes ( 0.0 MB)  1 5 5 546
Tensor  24 BoxPredictor_2/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  25 BoxPredictor_2/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     209664 bytes ( 0.2 MB)  546 1 1 384
Tensor  26 BoxPredictor_2/Reshape kTfLiteUInt8  kTfLiteArenaRw        600 bytes ( 0.0 MB)  1 150 1 4
Tensor  27 BoxPredictor_2/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw      13650 bytes ( 0.0 MB)  1 150 91
Tensor  28 BoxPredictor_2/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  29 BoxPredictor_2/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  30 BoxPredictor_3/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw        216 bytes ( 0.0 MB)  1 3 3 24
Tensor  31 BoxPredictor_3/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  32 BoxPredictor_3/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       4608 bytes ( 0.0 MB)  24 1 1 192
Tensor  33 BoxPredictor_3/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw       4914 bytes ( 0.0 MB)  1 3 3 546
Tensor  34 BoxPredictor_3/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  35 BoxPredictor_3/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     104832 bytes ( 0.1 MB)  546 1 1 192
Tensor  36 BoxPredictor_3/Reshape kTfLiteUInt8  kTfLiteArenaRw        216 bytes ( 0.0 MB)  1 54 1 4
Tensor  37 BoxPredictor_3/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw       4914 bytes ( 0.0 MB)  1 54 91
Tensor  38 BoxPredictor_3/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  39 BoxPredictor_3/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  40 BoxPredictor_4/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw         96 bytes ( 0.0 MB)  1 2 2 24
Tensor  41 BoxPredictor_4/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  42 BoxPredictor_4/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       4608 bytes ( 0.0 MB)  24 1 1 192
Tensor  43 BoxPredictor_4/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw       2184 bytes ( 0.0 MB)  1 2 2 546
Tensor  44 BoxPredictor_4/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  45 BoxPredictor_4/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     104832 bytes ( 0.1 MB)  546 1 1 192
Tensor  46 BoxPredictor_4/Reshape kTfLiteUInt8  kTfLiteArenaRw         96 bytes ( 0.0 MB)  1 24 1 4
Tensor  47 BoxPredictor_4/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw       2184 bytes ( 0.0 MB)  1 24 91
Tensor  48 BoxPredictor_4/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  49 BoxPredictor_4/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  50 BoxPredictor_5/BoxEncodingPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw         24 bytes ( 0.0 MB)  1 1 1 24
Tensor  51 BoxPredictor_5/BoxEncodingPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  52 BoxPredictor_5/BoxEncodingPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       2304 bytes ( 0.0 MB)  24 1 1 96
Tensor  53 BoxPredictor_5/ClassPredictor/BiasAdd kTfLiteUInt8  kTfLiteArenaRw        546 bytes ( 0.0 MB)  1 1 1 546
Tensor  54 BoxPredictor_5/ClassPredictor/Conv2D_bias kTfLiteInt32   kTfLiteMmapRo       2184 bytes ( 0.0 MB)  546
Tensor  55 BoxPredictor_5/ClassPredictor/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      52416 bytes ( 0.0 MB)  546 1 1 96
Tensor  56 BoxPredictor_5/Reshape kTfLiteUInt8  kTfLiteArenaRw         24 bytes ( 0.0 MB)  1 6 1 4
Tensor  57 BoxPredictor_5/Reshape_1 kTfLiteUInt8  kTfLiteArenaRw        546 bytes ( 0.0 MB)  1 6 91
Tensor  58 BoxPredictor_5/stack kTfLiteInt32   kTfLiteMmapRo         16 bytes ( 0.0 MB)  4
Tensor  59 BoxPredictor_5/stack_1 kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor  60 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_192/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor  61 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_192/Relu6 kTfLiteUInt8  kTfLiteArenaRw      19200 bytes ( 0.0 MB)  1 10 10 192
Tensor  62 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_2_1x1_192/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  192 1 1 768
Tensor  63 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_96/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor  64 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_96/Relu6 kTfLiteUInt8  kTfLiteArenaRw       2400 bytes ( 0.0 MB)  1 5 5 96
Tensor  65 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_3_1x1_96/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      36864 bytes ( 0.0 MB)  96 1 1 384
Tensor  66 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_96/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor  67 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_96/Relu6 kTfLiteUInt8  kTfLiteArenaRw        864 bytes ( 0.0 MB)  1 3 3 96
Tensor  68 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_4_1x1_96/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      18432 bytes ( 0.0 MB)  96 1 1 192
Tensor  69 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_48/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        192 bytes ( 0.0 MB)  48
Tensor  70 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_48/Relu6 kTfLiteUInt8  kTfLiteArenaRw        192 bytes ( 0.0 MB)  1 2 2 48
Tensor  71 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_1_Conv2d_5_1x1_48/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       9216 bytes ( 0.0 MB)  48 1 1 192
Tensor  72 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_384/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  73 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_384/Relu6 kTfLiteUInt8  kTfLiteArenaRw       9600 bytes ( 0.0 MB)  1 5 5 384
Tensor  74 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_2_3x3_s2_384/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     663552 bytes ( 0.6 MB)  384 3 3 192
Tensor  75 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_192/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor  76 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_192/Relu6 kTfLiteUInt8  kTfLiteArenaRw       1728 bytes ( 0.0 MB)  1 3 3 192
Tensor  77 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_3_3x3_s2_192/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     165888 bytes ( 0.2 MB)  192 3 3 96
Tensor  78 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_192/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor  79 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_192/Relu6 kTfLiteUInt8  kTfLiteArenaRw        768 bytes ( 0.0 MB)  1 2 2 192
Tensor  80 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_4_3x3_s2_192/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     165888 bytes ( 0.2 MB)  192 3 3 96
Tensor  81 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_96/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor  82 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_96/Relu6 kTfLiteUInt8  kTfLiteArenaRw         96 bytes ( 0.0 MB)  1 1 1 96
Tensor  83 FeatureExtractor/MobilenetV1/Conv2d_13_pointwise_2_Conv2d_5_3x3_s2_96/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      41472 bytes ( 0.0 MB)  96 3 3 48
Tensor  84 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor  85 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 150 150 24
Tensor  86 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        648 bytes ( 0.0 MB)  24 3 3 3
Tensor  87 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor  88 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  89 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor  90 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  91 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor  92 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_10_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor  93 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor  94 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  95 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor  96 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor  97 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor  98 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_11_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor  99 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      38400 bytes ( 0.0 MB)  1 10 10 384
Tensor 100 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 101 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor 102 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       3072 bytes ( 0.0 MB)  768
Tensor 103 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      76800 bytes ( 0.1 MB)  1 10 10 768
Tensor 104 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_12_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     294912 bytes ( 0.3 MB)  768 1 1 384
Tensor 105 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      76800 bytes ( 0.1 MB)  1 10 10 768
Tensor 106 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       3072 bytes ( 0.0 MB)  768
Tensor 107 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       6912 bytes ( 0.0 MB)  1 3 3 768
Tensor 108 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       3072 bytes ( 0.0 MB)  768
Tensor 109 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      76800 bytes ( 0.1 MB)  1 10 10 768
Tensor 110 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_13_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     589824 bytes ( 0.6 MB)  768 1 1 768
Tensor 111 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 150 150 24
Tensor 112 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo         96 bytes ( 0.0 MB)  24
Tensor 113 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        216 bytes ( 0.0 MB)  1 3 3 24
Tensor 114 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        192 bytes ( 0.0 MB)  48
Tensor 115 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw    1080000 bytes ( 1.0 MB)  1 150 150 48
Tensor 116 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_1_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       1152 bytes ( 0.0 MB)  48 1 1 24
Tensor 117 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     270000 bytes ( 0.3 MB)  1 75 75 48
Tensor 118 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        192 bytes ( 0.0 MB)  48
Tensor 119 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        432 bytes ( 0.0 MB)  1 3 3 48
Tensor 120 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor 121 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 75 75 96
Tensor 122 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_2_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       4608 bytes ( 0.0 MB)  96 1 1 48
Tensor 123 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 75 75 96
Tensor 124 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor 125 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        864 bytes ( 0.0 MB)  1 3 3 96
Tensor 126 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor 127 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     540000 bytes ( 0.5 MB)  1 75 75 96
Tensor 128 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_3_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       9216 bytes ( 0.0 MB)  96 1 1 96
Tensor 129 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 38 38 96
Tensor 130 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        384 bytes ( 0.0 MB)  96
Tensor 131 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo        864 bytes ( 0.0 MB)  1 3 3 96
Tensor 132 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor 133 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     277248 bytes ( 0.3 MB)  1 38 38 192
Tensor 134 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_4_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      18432 bytes ( 0.0 MB)  192 1 1 96
Tensor 135 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     277248 bytes ( 0.3 MB)  1 38 38 192
Tensor 136 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor 137 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       1728 bytes ( 0.0 MB)  1 3 3 192
Tensor 138 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor 139 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     277248 bytes ( 0.3 MB)  1 38 38 192
Tensor 140 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_5_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      36864 bytes ( 0.0 MB)  192 1 1 192
Tensor 141 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw      69312 bytes ( 0.1 MB)  1 19 19 192
Tensor 142 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo        768 bytes ( 0.0 MB)  192
Tensor 143 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       1728 bytes ( 0.0 MB)  1 3 3 192
Tensor 144 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 145 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 146 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_6_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo      73728 bytes ( 0.1 MB)  384 1 1 192
Tensor 147 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 148 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 149 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor 150 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 151 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 152 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_7_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor 153 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 154 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 155 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor 156 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 157 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 158 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_8_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor 159 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 160 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/depthwise_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 161 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_depthwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo       3456 bytes ( 0.0 MB)  1 3 3 384
Tensor 162 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Conv2D_Fold_bias kTfLiteInt32   kTfLiteMmapRo       1536 bytes ( 0.0 MB)  384
Tensor 163 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/Relu6 kTfLiteUInt8  kTfLiteArenaRw     138624 bytes ( 0.1 MB)  1 19 19 384
Tensor 164 FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_9_pointwise/weights_quant/FakeQuantWithMinMaxVars kTfLiteUInt8   kTfLiteMmapRo     147456 bytes ( 0.1 MB)  384 1 1 384
Tensor 165 Squeeze              kTfLiteUInt8  kTfLiteArenaRw       7668 bytes ( 0.0 MB)  1 1917 4
Tensor 166 Squeeze_shape        kTfLiteInt32   kTfLiteMmapRo         12 bytes ( 0.0 MB)  3
Tensor 167 TFLite_Detection_PostProcess kTfLiteFloat32  kTfLiteArenaRw        160 bytes ( 0.0 MB)  1 10 4
Tensor 168 TFLite_Detection_PostProcess:1 kTfLiteFloat32  kTfLiteArenaRw         40 bytes ( 0.0 MB)  1 10
Tensor 169 TFLite_Detection_PostProcess:2 kTfLiteFloat32  kTfLiteArenaRw         40 bytes ( 0.0 MB)  1 10
Tensor 170 TFLite_Detection_PostProcess:3 kTfLiteFloat32  kTfLiteArenaRw          4 bytes ( 0.0 MB)  1
Tensor 171 anchors              kTfLiteUInt8   kTfLiteMmapRo       7668 bytes ( 0.0 MB)  1917 4
Tensor 172 concat               kTfLiteUInt8  kTfLiteArenaRw       7668 bytes ( 0.0 MB)  1 1917 1 4
Tensor 173 concat_1             kTfLiteUInt8  kTfLiteArenaRw     174447 bytes ( 0.2 MB)  1 1917 91
Tensor 174 convert_scores       kTfLiteUInt8  kTfLiteArenaRw     174447 bytes ( 0.2 MB)  1 1917 91
Tensor 175 normalized_input_image_tensor kTfLiteUInt8  kTfLiteArenaRw     270000 bytes ( 0.3 MB)  1 300 300 3
Tensor 176 (null)               kTfLiteFloat32  kTfLiteArenaRw      30672 bytes ( 0.0 MB)  1917 4
Tensor 177 (null)               kTfLiteFloat32  kTfLiteArenaRw     697788 bytes ( 0.7 MB)  1917 91
Tensor 178 (null)               kTfLiteUInt8  kTfLiteArenaRw       1917 bytes ( 0.0 MB)  1917
Tensor 179 (null)               kTfLiteUInt8  kTfLiteArenaRw     607500 bytes ( 0.6 MB)  1 150 150 27
Tensor 180 (null)               kTfLiteUInt8  kTfLiteArenaRw      43200 bytes ( 0.0 MB)  1 5 5 1728
Tensor 181 (null)               kTfLiteUInt8  kTfLiteArenaRw       7776 bytes ( 0.0 MB)  1 3 3 864
Tensor 182 (null)               kTfLiteUInt8  kTfLiteArenaRw       3456 bytes ( 0.0 MB)  1 2 2 864
Tensor 183 (null)               kTfLiteUInt8  kTfLiteArenaRw        432 bytes ( 0.0 MB)  1 1 1 432

Node   0 Operator Builtin Code   3 CONV_2D
  Inputs: 175 86 84
  Outputs: 85
  Temporaries: 179
Node   1 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 85 113 112
  Outputs: 111
Node   2 Operator Builtin Code   3 CONV_2D
  Inputs: 111 116 114
  Outputs: 115
Node   3 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 115 119 118
  Outputs: 117
Node   4 Operator Builtin Code   3 CONV_2D
  Inputs: 117 122 120
  Outputs: 121
Node   5 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 121 125 124
  Outputs: 123
Node   6 Operator Builtin Code   3 CONV_2D
  Inputs: 123 128 126
  Outputs: 127
Node   7 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 127 131 130
  Outputs: 129
Node   8 Operator Builtin Code   3 CONV_2D
  Inputs: 129 134 132
  Outputs: 133
Node   9 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 133 137 136
  Outputs: 135
Node  10 Operator Builtin Code   3 CONV_2D
  Inputs: 135 140 138
  Outputs: 139
Node  11 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 139 143 142
  Outputs: 141
Node  12 Operator Builtin Code   3 CONV_2D
  Inputs: 141 146 144
  Outputs: 145
Node  13 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 145 149 148
  Outputs: 147
Node  14 Operator Builtin Code   3 CONV_2D
  Inputs: 147 152 150
  Outputs: 151
Node  15 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 151 155 154
  Outputs: 153
Node  16 Operator Builtin Code   3 CONV_2D
  Inputs: 153 158 156
  Outputs: 157
Node  17 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 157 161 160
  Outputs: 159
Node  18 Operator Builtin Code   3 CONV_2D
  Inputs: 159 164 162
  Outputs: 163
Node  19 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 163 89 88
  Outputs: 87
Node  20 Operator Builtin Code   3 CONV_2D
  Inputs: 87 92 90
  Outputs: 91
Node  21 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 91 95 94
  Outputs: 93
Node  22 Operator Builtin Code   3 CONV_2D
  Inputs: 93 98 96
  Outputs: 97
Node  23 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 97 101 100
  Outputs: 99
Node  24 Operator Builtin Code   3 CONV_2D
  Inputs: 99 104 102
  Outputs: 103
Node  25 Operator Builtin Code   4 DEPTHWISE_CONV_2D
  Inputs: 103 107 106
  Outputs: 105
Node  26 Operator Builtin Code   3 CONV_2D
  Inputs: 105 110 108
  Outputs: 109
Node  27 Operator Builtin Code   3 CONV_2D
  Inputs: 109 62 60
  Outputs: 61
Node  28 Operator Builtin Code   3 CONV_2D
  Inputs: 61 74 72
  Outputs: 73
  Temporaries: 180
Node  29 Operator Builtin Code   3 CONV_2D
  Inputs: 73 65 63
  Outputs: 64
Node  30 Operator Builtin Code   3 CONV_2D
  Inputs: 64 77 75
  Outputs: 76
  Temporaries: 181
Node  31 Operator Builtin Code   3 CONV_2D
  Inputs: 76 68 66
  Outputs: 67
Node  32 Operator Builtin Code   3 CONV_2D
  Inputs: 67 80 78
  Outputs: 79
  Temporaries: 182
Node  33 Operator Builtin Code   3 CONV_2D
  Inputs: 79 71 69
  Outputs: 70
Node  34 Operator Builtin Code   3 CONV_2D
  Inputs: 70 83 81
  Outputs: 82
  Temporaries: 183
Node  35 Operator Builtin Code   3 CONV_2D
  Inputs: 97 2 1
  Outputs: 0
Node  36 Operator Builtin Code  22 RESHAPE
  Inputs: 0 8
  Outputs: 6
Node  37 Operator Builtin Code   3 CONV_2D
  Inputs: 97 5 4
  Outputs: 3
Node  38 Operator Builtin Code  22 RESHAPE
  Inputs: 3 9
  Outputs: 7
Node  39 Operator Builtin Code   3 CONV_2D
  Inputs: 109 12 11
  Outputs: 10
Node  40 Operator Builtin Code  22 RESHAPE
  Inputs: 10 18
  Outputs: 16
Node  41 Operator Builtin Code   3 CONV_2D
  Inputs: 109 15 14
  Outputs: 13
Node  42 Operator Builtin Code  22 RESHAPE
  Inputs: 13 19
  Outputs: 17
Node  43 Operator Builtin Code   3 CONV_2D
  Inputs: 73 22 21
  Outputs: 20
Node  44 Operator Builtin Code  22 RESHAPE
  Inputs: 20 28
  Outputs: 26
Node  45 Operator Builtin Code   3 CONV_2D
  Inputs: 73 25 24
  Outputs: 23
Node  46 Operator Builtin Code  22 RESHAPE
  Inputs: 23 29
  Outputs: 27
Node  47 Operator Builtin Code   3 CONV_2D
  Inputs: 76 32 31
  Outputs: 30
Node  48 Operator Builtin Code  22 RESHAPE
  Inputs: 30 38
  Outputs: 36
Node  49 Operator Builtin Code   3 CONV_2D
  Inputs: 76 35 34
  Outputs: 33
Node  50 Operator Builtin Code  22 RESHAPE
  Inputs: 33 39
  Outputs: 37
Node  51 Operator Builtin Code   3 CONV_2D
  Inputs: 79 42 41
  Outputs: 40
Node  52 Operator Builtin Code  22 RESHAPE
  Inputs: 40 48
  Outputs: 46
Node  53 Operator Builtin Code   3 CONV_2D
  Inputs: 79 45 44
  Outputs: 43
Node  54 Operator Builtin Code  22 RESHAPE
  Inputs: 43 49
  Outputs: 47
Node  55 Operator Builtin Code   3 CONV_2D
  Inputs: 82 52 51
  Outputs: 50
Node  56 Operator Builtin Code  22 RESHAPE
  Inputs: 50 58
  Outputs: 56
Node  57 Operator Builtin Code   2 CONCATENATION
  Inputs: 6 16 26 36 46 56
  Outputs: 172
Node  58 Operator Builtin Code  22 RESHAPE
  Inputs: 172 166
  Outputs: 165
Node  59 Operator Builtin Code   3 CONV_2D
  Inputs: 82 55 54
  Outputs: 53
Node  60 Operator Builtin Code  22 RESHAPE
  Inputs: 53 59
  Outputs: 57
Node  61 Operator Builtin Code   2 CONCATENATION
  Inputs: 7 17 27 37 47 57
  Outputs: 173
Node  62 Operator Builtin Code  14 LOGISTIC
  Inputs: 173
  Outputs: 174
Node  63 Operator Custom Name TFLite_Detection_PostProcess
  Inputs: 165 174 171
  Outputs: 167 168 169 170
  Temporaries: 176 177 178
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프로그램 사용/rtl-sdr2021. 1. 16. 16:57

Blaze Video / FM 듣기

아는 분에게 USB DVD/CD-ROM 빌려서 드라이버랑 프로그램 CD에서 추출해서 설치하니

되긴 한데 안된다고 해야하나...?

 

아무튼 안테나도 거의 3M 이상 길게 해야지 겨우 2채널 잡혔는데

프로그램 쓰는법을 몰라서 수동으로 채널을 잡는법을 모르겠다.

 

시작 스플래시 이미지

 

TV 버튼을 누르면 DVB-T / DAB / FM이 뜬다.

처음에는 TV만 되는줄 알고 헉 했네..

스캔 1차 시도 -_-

 

안테나 연장해서 다시 하니 겨우 2개 잡힌다.

잡힌 2개 채널 89.1MHz 랑

 

89.7MHz 두개 인듯.

 

채널을 편집할수가 없네..

RTL-SDR 보다는 확실히 CPU를 조금 먹는것 같긴한데

RTL-SDR을 설치하면 윈도우에서 또 돌리기 귀찮으니 고민중..

 

해당 어플리케이션은 G4600 에서 1.4% 정도 먹는데

RTL-SDR도 대충 10% 는 먹었던것 같은데 Gain 건드릴 수 있으면 좀 더 나아지려나?

 

+

안테나 선길이가 점점 길어지는구나 ㅋㅋㅋ

대충 재보니 380cm ㅋㅋㅋㅋ 회로에 쓰는 얇은 선으로 안테나 처럼 만들었는데 고작 5개라니 ㅠㅠㅠ

gqrx에서 확인해보니

89.1 -9 dBFS

89.7 -14 dBFS

98.1 -7 dBFS

93.1 -11 dBFS

93.6 -12 dBFS(삐~ 소리, 대남방송 재밍)

95.1 -13 dBFS

---

91.9 -16 dBFS

93.9 -18 dBFS

 

dBFS가 무슨 단위인지 모르겠지만 RTL-SDR이 잡음이 생겨도 들을순 있다면

RTL Driver를 이용한 FM은 노이즈가 안들릴정도로 깔끔해야만 잡힌다.

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프로그램 사용/yolo_tensorflow2021. 1. 14. 16:59

mobileNET/SSD

python 안쓰고 c++ 만으로 해보려는데

tensorflow / rosrun 등이 필요함

mobilenet ssd
tensorflow /c++/python
catkin_make

[링크 : https://github.com/haosen9527/mobileNet-ssd]

 

빌드는 독립적으로 되는데 caffe를 먼저 빌드는 해야 함..

caffe를 빌드하는데 openCV-3.4.0 이상을 요구하고 있음..

Also it may help the beginners to build a project using cmake. You don't need to build this within the caffe root. But remember you should compile caffe first.

[링크 : https://github.com/MediosZ/MobileNet-SSD]

 

+

python으로 된 가장 원본 파일?

[링크 : https://github.com/chuanqi305/MobileNet-SSD]

 

+

tensorflow lite 버전과 openCV-4.1.0 이상 버전 요구?

[링크 : https://github.com/finnickniu/tensorflow_object_detection_tflite]

 

+

rpi 2b에서 텐서가 되는지 모르겠네?

[링크 : https://www.tensorflow.org/install/source_rpi?hl=ko]

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프로그램 사용/yolo_tensorflow2021. 1. 14. 11:11

caffe

 

[링크 : https://caffe.berkeleyvision.org/]

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프로그램 사용/Version Control2021. 1. 11. 12:21

git error: RPC failed; curl ... remote end hung up

depth 문제라면.. 설마 압축하다 서버 뻗는건가?

 

[링크 : https://dukeyang.tistory.com/16]

[링크 : https://stackoverflow.com/questions/38618885]

 

에러가 한글로 나오면서 "early EOF"가 "너무 빨리 파일이 끝남" 로 나와서 좀 헤맸네

fatal: read error: Invalid argument, 255.05 MiB | 1.35 MiB/s
fatal: early EOF
fatal: index-pack failed

 

결론은.. 서버쪽 문제가 맞나보네

[링크 : https://www.lesstif.com/gitbook/git-clone-fatal-early-eof-fatal-index-pack-failed-95879326.html]

[링크 : https://pakss328.medium.com/git-early-eof-index-pack-failed-에러-처리-1d9d0357124b]

 

 

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프로그램 사용/yolo_tensorflow2021. 1. 11. 11:37

darknet detect

외부에서 획득한 cfg와 weight 로 실행을 하려고 하니 엉뚱하게 bicycle,person,car가 나오고 있어서 급 멘붕 -_-

/darknet detect cfg/yolov3.cfg yolov3.weights data/dog.jpg

 

 

그래서 코드를 뜯어보니 detect 로 실행 할 경우

test_detector를 실행하게 하고 cfg/coco.data 라는걸 자동으로 사용하게 한다.

    if (0 == strcmp(argv[1], "average")){
        average(argc, argv);
    } else if (0 == strcmp(argv[1], "yolo")){
        run_yolo(argc, argv);
    } else if (0 == strcmp(argv[1], "voxel")){
        run_voxel(argc, argv);
    } else if (0 == strcmp(argv[1], "super")){
        run_super(argc, argv);
    } else if (0 == strcmp(argv[1], "detector")){
        run_detector(argc, argv);
    } else if (0 == strcmp(argv[1], "detect")){
        float thresh = find_float_arg(argc, argv, "-thresh", .24);
		int ext_output = find_arg(argc, argv, "-ext_output");
        char *filename = (argc > 4) ? argv[4]: 0;
        test_detector("cfg/coco.data", argv[2], argv[3], filename, thresh, 0.5, 0, ext_output, 0, NULL, 0, 0);
    }

 

 coco.data에는 별 건 없는것 같은데 가장 중요한(?)건 names 인 것 같고

$ cat cfg/coco.data
classes= 80
train  = /home/pjreddie/data/coco/trainvalno5k.txt
valid  = coco_testdev
#valid = data/coco_val_5k.list
names = data/coco.names
backup = /home/pjreddie/backup/
eval=coco

 

coco.names 에는 항목별 이름이 존재하는 것을 확인할 수 있었다.

$ cat data/coco.names
person
bicycle
car
motorbike
aeroplane
bus
train
truck
boat
traffic light
fire hydrant
stop sign
parking meter
bench
bird
cat
dog
horse
sheep
cow
elephant
bear
zebra
giraffe
backpack
umbrella
handbag
tie
suitcase
frisbee
skis
snowboard
sports ball
kite
baseball bat
baseball glove
skateboard
surfboard
tennis racket
bottle
wine glass
cup
fork
knife
spoon
bowl
banana
apple
sandwich
orange
broccoli
carrot
hot dog
pizza
donut
cake
chair
sofa
pottedplant
bed
diningtable
toilet
tvmonitor
laptop
mouse
remote
keyboard
cell phone
microwave
oven
toaster
sink
refrigerator
book
clock
vase
scissors
teddy bear
hair drier
toothbrush

 

그러니까..

결국에는 최소 5개의 파일이 필요한건가?

cfg, weight, data, name 그리고 테스트 이미지

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프로그램 사용/yolo_tensorflow2021. 1. 10. 11:37

CNN convolution과 maxpool

pooling  - overfitting 방지

[링크 : https://hobinjeong.medium.com/cnn에서-pooling이란-c4e01aa83c83]

[링크 : http://hobinjeong.medium.com/cnn-convolutional-neural-network-9f600dd3b66395]

 

정리 잘된 동영상이 있어서 링크

 

convolution은 특정 신호에 반응하고 이미지 어디에 있는지를 확인하고

pooling은 위치나 각도에 둔감해지도록 인식율을 올리는 효과를 지니는 연산(?)

[링크 : https://www.youtube.com/watch?v=u0eT7VZAgRw]

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