tf.size() tflite 호환 연산자 목록에 없긴한데

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

 

tf.size()는... tflite 지원인지 아닌지 말이 없네?

[링크 : https://www.tensorflow.org/api_docs/python/tf/size?hl=ko]

 

하라는 대로 하니 변환은 되었다?

아무래도 tflite_convert에는 없는 SELECT_TF_OPS 같은게 있어서 그런가?

import tensorflow as tf

converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.target_spec.supported_ops = [
  tf.lite.OpsSet.TFLITE_BUILTINS, # enable TensorFlow Lite ops.
  tf.lite.OpsSet.SELECT_TF_OPS # enable TensorFlow ops.
]
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)

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

  [링크 : https://stackoverflow.com/questions/53824223/what-does-flex-op-mean-in-tensorflow]

 

일단은 먼진 몰라도 그냥 pb가 있는거 대충 받는 링크

[링크 : https://tfhub.dev/tensorflow/ssd_mobilenet_v2/2]

[링크 : https://tfhub.dev/tensorflow/retinanet/resnet101_v1_fpn_1024x1024/1]

 

tflite-converter.py 에서 뒤져보니 아래와 같은 옵션을 주어야 작동하는건가..?

  if flags.experimental_select_user_tf_ops:
    if lite.OpsSet.SELECT_TF_OPS not in converter.target_spec.supported_ops:
      raise ValueError("--experimental_select_user_tf_ops can only be set if "
                       "--target_ops contains SELECT_TF_OPS.")

 

+

변환한 파일을 실행하니 죽는다 -_ㅠ overflowed.. 무시무시한 에러인데?

$ ./label_image -i 2012060407491899196_l.jpg -m test.tflite 
INFO: Loaded model go.tflite
INFO: resolved reporter
ERROR: tensorflow/lite/core/subgraph.cc BytesRequired number of elements overflowed.

[링크 : https://stackoverflow.com/questions/63500096/]

Posted by 구차니

변환하기 힘들다 ㅠㅠ

실행해보니 pb나 tflite 를 읽어서 시각화 하는데

tensorboard랑 비슷하다고 해야하나 다르다고 하나.. 보이는 방식은 가로로(tensroboard) 세로로(netron) 보이는 차이인것 같기도 하고..

다만 버전정보가 똭 보이는건 편하네..

그리고 tensorboard보다는 빠른 느낌이다.

 

[링크 : https://devinlife.com/tensorflow%20lite/tflite-simple-regression/]

[링크 : https://github.com/lutzroeder/netron]

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

tensorflowlite build  (0) 2021.01.28
pb to tflite 변환 part 2...  (0) 2021.01.27
pb to tflite, tensorboard  (0) 2021.01.26
tensorflow pb to tflite  (0) 2021.01.25
텐서플로우 - detection과 classification  (0) 2021.01.22
Posted by 구차니

tensorboard

먼가 복잡하게 나오는데 보는법을 모르겠다? ㅠㅠ

[링크 : https://urbangy.tistory.com/38]

[링크 : https://eehoeskrap.tistory.com/322]

 

pb to tflite

영... 실패중.. ㅠㅠ

[링크 : https://github.com/tensorflow/tensorflow/issues/46285]

 

$ python3 /home/minimonk/src/tensorflow/tensorflow/lite/python/tflite_convert.py --saved_model_dir=./saved_model --output_file=output.tflite
2021-01-26 19:01:39.223104: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory
2021-01-26 19:01:39.223142: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.
2021-01-26 19:01:41.278842: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-26 19:01:41.279042: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory
2021-01-26 19:01:41.279063: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
2021-01-26 19:01:41.279101: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (mini2760p): /proc/driver/nvidia/version does not exist
2021-01-26 19:01:41.279527: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-26 19:01:55.229040: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format.
2021-01-26 19:01:55.229092: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency.
2021-01-26 19:01:55.229117: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:325] Ignored change_concat_input_ranges.
2021-01-26 19:01:55.230250: I tensorflow/cc/saved_model/reader.cc:32] Reading SavedModel from: ./saved_model
2021-01-26 19:01:55.349428: I tensorflow/cc/saved_model/reader.cc:55] Reading meta graph with tags { serve }
2021-01-26 19:01:55.349498: I tensorflow/cc/saved_model/reader.cc:93] Reading SavedModel debug info (if present) from: ./saved_model
2021-01-26 19:01:55.349576: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-01-26 19:01:55.676408: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)
2021-01-26 19:01:55.748285: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.
2021-01-26 19:01:55.826459: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2494460000 Hz
2021-01-26 19:01:56.738523: I tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: ./saved_model
2021-01-26 19:01:57.100034: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 1869785 microseconds.
2021-01-26 19:01:58.857435: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:194] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2021-01-26 19:01:59.851936: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
loc(callsite(callsite("Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/SortByField_1/Size@__inference_call_func_10155" at "StatefulPartitionedCall@__inference_signature_wrapper_11818") at "StatefulPartitionedCall")): error: 'tf.Size' op is neither a custom op nor a flex op
error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.Size {device = ""}
Traceback (most recent call last):
  File "/home/minimonk/.local/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 210, in toco_convert_protos
    model_str = wrap_toco.wrapped_toco_convert(model_flags_str,
  File "/home/minimonk/.local/lib/python3.8/site-packages/tensorflow/lite/python/wrap_toco.py", line 32, in wrapped_toco_convert
    return _pywrap_toco_api.TocoConvert(
Exception: <unknown>:0: error: loc(callsite(callsite("Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/SortByField_1/Size@__inference_call_func_10155" at "StatefulPartitionedCall@__inference_signature_wrapper_11818") at "StatefulPartitionedCall")): 'tf.Size' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.Size {device = ""}


During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/home/minimonk/src/tensorflow/tensorflow/lite/python/tflite_convert.py", line 698, in <module>
    main()
  File "/home/minimonk/src/tensorflow/tensorflow/lite/python/tflite_convert.py", line 694, in main
    app.run(main=run_main, argv=sys.argv[:1])
  File "/home/minimonk/.local/lib/python3.8/site-packages/tensorflow/python/platform/app.py", line 40, in run
    _run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
  File "/home/minimonk/.local/lib/python3.8/site-packages/absl/app.py", line 303, in run
    _run_main(main, args)
  File "/home/minimonk/.local/lib/python3.8/site-packages/absl/app.py", line 251, in _run_main
    sys.exit(main(argv))
  File "/home/minimonk/src/tensorflow/tensorflow/lite/python/tflite_convert.py", line 677, in run_main
    _convert_tf2_model(tflite_flags)
  File "/home/minimonk/src/tensorflow/tensorflow/lite/python/tflite_convert.py", line 265, in _convert_tf2_model
    tflite_model = converter.convert()
  File "/home/minimonk/.local/lib/python3.8/site-packages/tensorflow/lite/python/lite.py", line 739, in convert
    result = _convert_saved_model(**converter_kwargs)
  File "/home/minimonk/.local/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 632, in convert_saved_model
    data = toco_convert_protos(
  File "/home/minimonk/.local/lib/python3.8/site-packages/tensorflow/lite/python/convert.py", line 216, in toco_convert_protos
    raise ConverterError(str(e))
tensorflow.lite.python.convert.ConverterError: <unknown>:0: error: loc(callsite(callsite("Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/SortByField_1/Size@__inference_call_func_10155" at "StatefulPartitionedCall@__inference_signature_wrapper_11818") at "StatefulPartitionedCall")): 'tf.Size' op is neither a custom op nor a flex op
<unknown>:0: note: loc("StatefulPartitionedCall"): called from
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
	tf.Size {device = ""}

 

[링크 : https://bekusib.tistory.com/210]

[링크 : https://bugloss-chestnut.tistory.com/entry/Tensorflow-keras-h5-pb-tflite-변환-오류python]

[링크 : https://gmground.tistory.com/entry/학습된-모델을-TensorFlow-Lite-모델tflite로-변환하여-Android에서-Object-Classification-해보기]

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

pb to tflite 변환 part 2...  (0) 2021.01.27
tensorflow netron  (0) 2021.01.27
tensorflow pb to tflite  (0) 2021.01.25
텐서플로우 - detection과 classification  (0) 2021.01.22
텐서플로우 모델 출력? (metadata?)  (0) 2021.01.21
Posted by 구차니

saved_model 디렉토리 전체가 필요하군..

단순하게 pb2만 있으면 되는게 아니군아..

 

Converting a SavedModel
tflite_convert \
  --saved_model_dir=/tmp/mobilenet_saved_model \
  --output_file=/tmp/mobilenet.tflite

Converting a Keras H5 model
tflite_convert \
  --keras_model_file=/tmp/mobilenet_keras_model.h5 \
  --output_file=/tmp/mobilenet.tflite

[링크 : https://www.tensorflow.org/lite/convert]

[링크 : https://stackoverflow.com/questions/52918051/how-to-convert-pb-to-tflite-format]

Posted by 구차니

detection은 객체의 종류와 확률 그리고 "위치"를 얻는다면

[링크 : https://www.tensorflow.org/lite/models/object_detection/overview]

 

classification은 객체의 종류와 확률 만을 결과로 얻는다.

[링크 : https://www.tensorflow.org/lite/models/image_classification/overview]

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

pb to tflite, tensorboard  (0) 2021.01.26
tensorflow pb to tflite  (0) 2021.01.25
텐서플로우 모델 출력? (metadata?)  (0) 2021.01.21
tensorflow 객체 탐지 예제  (0) 2021.01.21
tensorflow lite on x86 / label_image  (0) 2021.01.20
Posted by 구차니

+ 2021.01.22

다시 정리하고 와서 보니

위에는 classification tflite 파일이고 아래는 detection 이니 차이가 나는 듯.

 

+

tflite 파일 vi로 보니 문자열들이 좀 보이는데, python 써서 출력이 가능하려나?

[링크 : https://www.tensorflow.org/lite/convert/metadata]

 

---

output_tensor_metadata 에서

name:probability 라는게 있는데

[링크 : https://tfhub.dev/google/lite-model/aiy/vision/classifier/food_V1/1]

[링크 : https://tfhub.dev/google/lite-model/cropnet/classifier/cassava_disease_V1/1]

 

다른 tfilte 파일에서 보니

output_tensor_groups 라는게 존재해서 tensor_names로 3개의 출력이 존재하고

output_tensor_metadata 에 name:location, name:category, name:score 가 존재한다.

[링크 : https://tfhub.dev/google/lite-model/object_detection/mobile_object_localizer_v1/1/metadata/2]

 

출력에서 여러개 출력을 내는건.. 해당 프로젝트에서 사용하는 tflite 파일이 그렇게 생성 되었기 때문인건가?

Output Signature
The model outputs four arrays, mapped to the indices 0-4. Arrays 0, 1, and 2 describe N detected objects, with one element in each array corresponding to each object.

[링크 : https://www.tensorflow.org/lite/models/object_detection/overview]

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

tensorflow pb to tflite  (0) 2021.01.25
텐서플로우 - detection과 classification  (0) 2021.01.22
tensorflow 객체 탐지 예제  (0) 2021.01.21
tensorflow lite on x86 / label_image  (0) 2021.01.20
tensorflow-lite 모델  (0) 2021.01.18
Posted by 구차니
Posted by 구차니

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]

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

tensorflow 객체 탐지 예제  (0) 2021.01.21
tensorflow lite on x86 / label_image  (0) 2021.01.20
tensorflow-lite minimal.cc 실행  (0) 2021.01.18
mobileNET/SSD  (0) 2021.01.14
caffe  (0) 2021.01.14
Posted by 구차니

+ 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

'프로그램 사용 > yolo_tensorflow' 카테고리의 다른 글

tensorflow lite on x86 / label_image  (0) 2021.01.20
tensorflow-lite 모델  (0) 2021.01.18
mobileNET/SSD  (0) 2021.01.14
caffe  (0) 2021.01.14
darknet detect  (0) 2021.01.11
Posted by 구차니