구차니의 잡동사니 모음

변환한 모델 실행하는데 에러가 발생

정확하게는 모델을 불러오다 죽는게 아닌가 싶어서 해당 파일을 열어봄

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

세그멘테이션 오류 (core dumped)

음.. 593 라인 에러라는데

 563 namespace {
 564 // Multiply two sizes and return true if overflow occurred;
 565 // This is based off tensorflow/overflow.h but is simpler as we already
 566 // have unsigned numbers. It is also generalized to work where sizeof(size_t)
 567 // is not 8. 
 568 TfLiteStatus MultiplyAndCheckOverflow(size_t a, size_t b, size_t* product) {
 569   // Multiplying a * b where a and b are size_t cannot result in overflow in a
 570   // size_t accumulator if both numbers have no non-zero bits in their upper
 571   // half.
 572   constexpr size_t size_t_bits = 8 * sizeof(size_t);
 573   constexpr size_t overflow_upper_half_bit_position = size_t_bits / 2;
 574   *product = a * b;
 575   // If neither integers have non-zero bits past 32 bits can't overflow.
 576   // Otherwise check using slow devision.
 577   if (TFLITE_EXPECT_FALSE((a | b) >> overflow_upper_half_bit_position != 0)) {
 578     if (a != 0 && *product / a != b) return kTfLiteError;
 579   }
 580   return kTfLiteOk;
 581 }  
 582 }  // namespace
 583 
 584 TfLiteStatus Subgraph::BytesRequired(TfLiteType type, const int* dims,
 585                                      size_t dims_size, size_t* bytes) {
 586   TF_LITE_ENSURE(&context_, bytes != nullptr);
 587   size_t count = 1;
 588   for (int k = 0; k < dims_size; k++) {
 589     size_t old_count = count;
 590     TF_LITE_ENSURE_MSG(
 591         &context_,
 592         MultiplyAndCheckOverflow(old_count, dims[k], &count) == kTfLiteOk,
 593         "BytesRequired number of elements overflowed.\n");
 594   }
 595   size_t type_size = 0;
 596   TF_LITE_ENSURE_OK(&context_, GetSizeOfType(&context_, type, &type_size));
 597   TF_LITE_ENSURE_MSG(
 598       &context_, MultiplyAndCheckOverflow(type_size, count, bytes) == kTfLiteOk,
 599       "BytesRequired number of bytes overflowed.\n");
 600   return kTfLiteOk;
 601 }

아래에서 설명은 들어도 dBFS가 어떤 의미로 쓰이고 이 값이 멀 의미하는지 모르겠다.

[링크 : http://audio-probe.com/documentation/db란-무엇인가/]

위조칩으로 인해서 막은건 기억을 했는데

이제는 인식은 하되 장치명을 이상하게 띄우고

시리얼 포트를 연결하지 않게 나온다.

물론 리눅스는 그런거 없이 잘 됨 ㅋ

minicom 으로 실행하면 폭이 늘어나지 않고 창도 안커지는데(putty)

아래와 같이 하면 창을 늘리면 같이 늘어난다.

[링크 : https://unix.stackexchange.com/questions/106644/how-to-change-the-width-of-remote-serial-console]

+물론 시리얼로 접속해서는 화면은 늘어나도 실제로 콘솔 영향을 받는지

더 위아래로 길게 나오진 않는다 ㅠㅠ

쓸일이 곧 생길게야.... (동공지진)

[링크 : https://github.com/torvalds/linux/blob/master/tools/spi/spidev_test.c]

+ 2021.02.08

It's easy to be confused here, and the vendor documentation you'll
find isn't necessarily helpful.  The four modes combine two mode bits:

 - CPOL indicates the initial clock polarity.  CPOL=0 means the
   clock starts low, so the first (leading) edge is rising, and
   the second (trailing) edge is falling.  CPOL=1 means the clock
   starts high, so the first (leading) edge is falling.

 - CPHA indicates the clock phase used to sample data; CPHA=0 says
   sample on the leading edge, CPHA=1 means the trailing edge.

   Since the signal needs to stablize before it's sampled, CPHA=0
   implies that its data is written half a clock before the first
   clock edge.  The chipselect may have made it become available.

[링크 : https://www.kernel.org/doc/Documentation/spi/spi-summary]

with_select_tf_ops 라는 옵션이 보이는데

*.cc 파일들 상에서 해당 디파인으로 뒤져도 안나오고 py 쪽으로만 보이는데..

헛짚은건가?  ㅠㅠ

bazel build --config=monolithic --define=with_select_tf_ops=true -c opt //tensorflow/lite:libtensorflowlite.so

[링크 : https://stackoverflow.com/questions/58623937/]

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/]

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]

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]

문득 생각이 들어 비싼 이어폰 써야 한대서

아부지 협찬으로 비싼(!) 이어폰 빌려서 해봤는데

내 기준으로는 그냥 볼륨이 커져서 작은 소리까지 잘 들리는 느낌?

그런 이유로 quad dac 켜면 미세하게 볼륨 조절되고 최대 볼륨이 더 커지는 게 개인적인 효과

다이나믹 레인지나 이런건 솔찍히 잘 모르겠고

en50332 이라는 표준에 의해 청각에 손상입지 않을 세기로 핸드폰 볼륨이 정해졌다고

[링크 : http://www.0db.co.kr/REVIEW_0DB/904118]

[링크 : https://www.clien.net/service/amp/board/park/11139522]

300옴

[링크 : http://danawa.com/product/product.html?code=74145&cateCode=12237350]