구차니의 잡동사니 모음

COCO 학습 시켜보기..

은근히 자료가 없네 ㅠㅠ

[링크 : https://tfhub.dev/tensorflow/ssd_mobilenet_v2/fpnlite_320x320/1]

  [링크 : http://github.com/tensorflow/models/tree/master/research/object_detection]

  [링크 : https://cocodataset.org/#home]

[링크 : https://heartbeat.fritz.ai/real-time-object-detection-using-ssd-mobilenet-v2-on-video-streams-3bfc1577399c]

[링크 : https://github.com/tensorflow/models/tree/master/official]

[링크 : https://github.com/abhimanyu1990/SSD-Mobilenet-Custom-Object-Detector-Model-using-Tensorflow-2]

[링크 : https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md]

해당예제를 조금 더 수정하면

UART1 에서 UART2로 서로 연결할 수 있겠네?

[링크 : https://riptutorial.com/stm32/example/29940/echo-application---hal-library]

UART1은 115200 UART2는 9600인데 크게 문제 없이 잘 되는 듯?

char byte;
char byte3;
void HAL_UART_RxCpltCallback(UART_HandleTypeDef *huart)
{
  if (huart->Instance == USART1)
  {
    /* Transmit one byte with 100 ms timeout */
    HAL_UART_Transmit(&huart3, &byte, 1, 100);

    /* Receive one byte in interrupt mode */
    HAL_UART_Receive_IT(&huart1, &byte, 1);
  }

  if (huart->Instance == USART3)
  {
    /* Transmit one byte with 100 ms timeout */
    HAL_UART_Transmit(&huart1, &byte3, 1, 100);

    /* Receive one byte in interrupt mode */
    HAL_UART_Receive_IT(&huart3, &byte3, 1);
  }
}


int main(void)
{
  HAL_UART_Receive_IT(&huart1, &byte, 1);
  HAL_UART_Receive_IT(&huart3, &byte3, 1);

  while (1)
  {

  }
}

STM32CubeIDE의 경우 ioc 파일을 수정하면 코드를 재성성하는데

소스내의 헤더들도 당연히(?) 재생성 되니 계속 원복 되어서 분노 폭발(!)

열심히 뒤적여 보니 uart callback 관련해서는

stm32f1xx_hal_conf.h 의 아래 부분 설정은

/* ########################### System Configuration ######################### */
/**
  * @brief This is the HAL system configuration section
  */
#define  VDD_VALUE                    3300U /*!< Value of VDD in mv */
#define  TICK_INT_PRIORITY            0U    /*!< tick interrupt priority (lowest by default)  */
#define  USE_RTOS                     0U
#define  PREFETCH_ENABLE              1U

#define  USE_HAL_ADC_REGISTER_CALLBACKS         0U /* ADC register callback disabled       */
#define  USE_HAL_CAN_REGISTER_CALLBACKS         0U /* CAN register callback disabled       */
#define  USE_HAL_CEC_REGISTER_CALLBACKS         0U /* CEC register callback disabled       */
#define  USE_HAL_DAC_REGISTER_CALLBACKS         0U /* DAC register callback disabled       */
#define  USE_HAL_ETH_REGISTER_CALLBACKS         0U /* ETH register callback disabled       */
#define  USE_HAL_HCD_REGISTER_CALLBACKS         0U /* HCD register callback disabled       */
#define  USE_HAL_I2C_REGISTER_CALLBACKS         0U /* I2C register callback disabled       */
#define  USE_HAL_I2S_REGISTER_CALLBACKS         0U /* I2S register callback disabled       */
#define  USE_HAL_MMC_REGISTER_CALLBACKS         0U /* MMC register callback disabled       */
#define  USE_HAL_NAND_REGISTER_CALLBACKS        0U /* NAND register callback disabled      */
#define  USE_HAL_NOR_REGISTER_CALLBACKS         0U /* NOR register callback disabled       */
#define  USE_HAL_PCCARD_REGISTER_CALLBACKS      0U /* PCCARD register callback disabled    */
#define  USE_HAL_PCD_REGISTER_CALLBACKS         0U /* PCD register callback disabled       */
#define  USE_HAL_RTC_REGISTER_CALLBACKS         0U /* RTC register callback disabled       */
#define  USE_HAL_SD_REGISTER_CALLBACKS          0U /* SD register callback disabled        */
#define  USE_HAL_SMARTCARD_REGISTER_CALLBACKS   0U /* SMARTCARD register callback disabled */
#define  USE_HAL_IRDA_REGISTER_CALLBACKS        0U /* IRDA register callback disabled      */
#define  USE_HAL_SRAM_REGISTER_CALLBACKS        0U /* SRAM register callback disabled      */
#define  USE_HAL_SPI_REGISTER_CALLBACKS         0U /* SPI register callback disabled       */
#define  USE_HAL_TIM_REGISTER_CALLBACKS         0U /* TIM register callback disabled       */
#define  USE_HAL_UART_REGISTER_CALLBACKS        1U /* UART register callback enabled      */
#define  USE_HAL_USART_REGISTER_CALLBACKS       0U /* USART register callback disabled     */
#define  USE_HAL_WWDG_REGISTER_CALLBACKS        0U /* WWDG register callback disabled      */

STM32CubeIDE의 ioc / Project Manager - Advanced Settings - Register Callbacks

(오른쪽 구석탱이...)을 Enable로 바꾸어 주면 된다.

연관이 있는 링크인진 모르겠음 ㅋㅋ

[링크 : https://www.st.com/.../dm00154093-description-of-stm32f1-hal-and-lowlayer-drivers-stmicroelectronics.pdf]

+

나도 어느걸 보다가 저걸 발견했는진 모르겠다 -_-

소스 뒤적여서 추적하다가 발견한건가..

[링크 : https://mul-ku.tistory.com/entry/STM32-UART-수신-인터럽트-사용법-및-간단한-예제HAL-DRIVER]

[링크 : https://community.st.com/s/question/0D53W000000bRmkSAE/stm32-uart-call-back-function]

[링크 : https://dkeemin.com/stm32f0-uart-수신-인터럽트-코드-작성하기/]

요 근래 업데이트가 되었는지

탭이 이상하게 나온다 -_-

오히려 한 화면에 보이는 양이 적어지는 느낌..

이전 설정으로 바꾸려고 부랴부랴 찾아봄

chrome://flags

페이지에서 tab 으로 검색해서 대충 disable / 재시작

[링크 : http://www.wetrend.co.kr/board/view?board_name=wit_board&wr_id=1308630]

ioc 파일을 통해 코드를 생성하는데

아래의 구역에 있는 애들은 새롭게 생성되어도 날아가지 않지만

그 외의 구역에는 전부 날아가니 주의!

/* USER CODE BEGIN 4 */

/* USER CODE END 4 */

SELECT_TF_OPS로 이리저리 찾으니, cmake 파일에서 똭?!

그런데 FATAL_ERROR.. 수상하다...

TODO: Add support.... 수상하다.....

 68 # This must be enabled when converting from TF models with SELECT_TF_OPS
 69 # enabled.
 70 # https://www.tensorflow.org/lite/guide/ops_select#converting_the_model
 71 # This is currently not supported.
 72 option(TFLITE_ENABLE_FLEX "Enable SELECT_TF_OPS" OFF) # TODO: Add support
 
197 if(TFLITE_ENABLE_FLEX)
198   message(FATAL_ERROR "TF Lite Flex delegate is currently not supported.")
199   populate_tflite_source_vars("delegates/flex" TFLITE_DELEGATES_FLEX_SRCS)
200   list(APPEND TFLITE_TARGET_DEPENDENCIES
201     absl::inlined_vector
202     absl::optional
203     absl::type_traits
204   )
205 endif()

아래처럼 하면 된다는데

sudo apt-get install cmake
git clone https://github.com/tensorflow/tensorflow.git tensorflow_src
mkdir tflite_build
cd tflite_build
cmake ../tensorflow_src/tensorflow/lite

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

안되서 -S 옵션주니 된....

야이 그지깽깽이들아!!!! ㅠㅠㅠ

CMake Error at CMakeLists.txt:198 (message):
  TF Lite Flex delegate is currently not supported.

어디까지 흘러가야 답을 찾을수 있을까 ㅠㅠ

아래 사이트에서 pb와 tflite를 모두 제공해주어서 한번 시도

[링크 : https://tfhub.dev/google/aiy/vision/classifier/birds_V1/1]

위 링크의 pb를 확인해보면 아래와 같이 나오는데, tag-set이 'serve' 라는게 없어서 변환하려니 안된다

$ saved_model_cli show --dir ./ --all
2021-02-02 16:42:12.474893: 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
2021-02-02 16:42:12.474941: 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.

MetaGraphDef with tag-set: '' contains the following SignatureDefs:

signature_def['default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['images'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 224, 224, 3)
        name: hub_input/images:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['default'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 965)
        name: prediction:0
  Method name is:

signature_def['image_classifier']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['images'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 224, 224, 3)
        name: hub_input/images:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['logits'] tensor_info:
        dtype: DT_FLOAT
        shape: (-1, 965)
        name: prediction:0
  Method name is:

이건 다른 모델.. 도대체 어떻게 봐야 하는걸까 ㅠㅠ

$ saved_model_cli show --dir ./ --all
2021-02-02 16:42:44.443703: 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
2021-02-02 16:42:44.443752: 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.

MetaGraphDef with tag-set: 'serve' contains the following SignatureDefs:

signature_def['__saved_model_init_op']:
  The given SavedModel SignatureDef contains the following input(s):
  The given SavedModel SignatureDef contains the following output(s):
    outputs['__saved_model_init_op'] tensor_info:
        dtype: DT_INVALID
        shape: unknown_rank
        name: NoOp
  Method name is:

signature_def['serving_default']:
  The given SavedModel SignatureDef contains the following input(s):
    inputs['input_tensor'] tensor_info:
        dtype: DT_UINT8
        shape: (1, -1, -1, 3)
        name: serving_default_input_tensor:0
  The given SavedModel SignatureDef contains the following output(s):
    outputs['detection_anchor_indices'] tensor_info:
        dtype: DT_FLOAT
        shape: (1, 100)
        name: StatefulPartitionedCall:0
    outputs['detection_boxes'] tensor_info:
        dtype: DT_FLOAT
        shape: (1, 100, 4)
        name: StatefulPartitionedCall:1
    outputs['detection_classes'] tensor_info:
        dtype: DT_FLOAT
        shape: (1, 100)
        name: StatefulPartitionedCall:2
    outputs['detection_multiclass_scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (1, 100, 91)
        name: StatefulPartitionedCall:3
    outputs['detection_scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (1, 100)
        name: StatefulPartitionedCall:4
    outputs['num_detections'] tensor_info:
        dtype: DT_FLOAT
        shape: (1)
        name: StatefulPartitionedCall:5
    outputs['raw_detection_boxes'] tensor_info:
        dtype: DT_FLOAT
        shape: (1, 1917, 4)
        name: StatefulPartitionedCall:6
    outputs['raw_detection_scores'] tensor_info:
        dtype: DT_FLOAT
        shape: (1, 1917, 91)
        name: StatefulPartitionedCall:7
  Method name is: tensorflow/serving/predict

Defined Functions:
  Function Name: '__call__'
    Option #1
      Callable with:
        Argument #1
          input_tensor: TensorSpec(shape=(1, None, None, 3), dtype=tf.uint8, name='input_tensor')

혹시나 해서 다시 한번 select_tf_ops 옵션을 빼고 해보았지만 역시나 안된다.

눈에 들어오는 에러는 아래것 정도인데 -emit-select-tf-ops 옵션을 누구에게 주어야 하는건지 모르겠다.

그리고 custom op nor flex op. flex op는 또 무엇인가...

tensorflow.lite.python.convert.ConverterError: <unknown>:0: error: loc(callsite(callsite("Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/SortByField_1/Size@__inference___call___21591" at "StatefulPartitionedCall@__inference_signature_wrapper_23250") 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 = ""}

$ cat c.py
import tensorflow as tf

saved_model_dir="./"
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)

$ python3 c.py
2021-02-02 16:45:08.234068: 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
2021-02-02 16:45:08.234112: 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.
2021-02-02 16:45:10.382490: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-02-02 16:45:10.382710: 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
2021-02-02 16:45:10.382742: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)
2021-02-02 16:45:10.382775: 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
2021-02-02 16:45:10.383265: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-02-02 16:45:26.331917: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:316] Ignored output_format.
2021-02-02 16:45:26.331970: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:319] Ignored drop_control_dependency.
2021-02-02 16:45:26.331981: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:325] Ignored change_concat_input_ranges.
2021-02-02 16:45:26.333113: I tensorflow/cc/saved_model/reader.cc:32] Reading SavedModel from: ./
2021-02-02 16:45:26.442652: I tensorflow/cc/saved_model/reader.cc:55] Reading meta graph with tags { serve }
2021-02-02 16:45:26.442721: I tensorflow/cc/saved_model/reader.cc:93] Reading SavedModel debug info (if present) from: ./
2021-02-02 16:45:26.442798: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-02-02 16:45:26.752919: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:196] None of the MLIR optimization passes are enabled (registered 0 passes)
2021-02-02 16:45:26.824027: I tensorflow/cc/saved_model/loader.cc:206] Restoring SavedModel bundle.
2021-02-02 16:45:26.900734: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2494085000 Hz
2021-02-02 16:45:27.788741: I tensorflow/cc/saved_model/loader.cc:190] Running initialization op on SavedModel bundle at path: ./
2021-02-02 16:45:28.227404: I tensorflow/cc/saved_model/loader.cc:277] SavedModel load for tags { serve }; Status: success: OK. Took 1894293 microseconds.
2021-02-02 16:45:34.080047: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:194] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.
2021-02-02 16:45:35.369335: 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___21591" at "StatefulPartitionedCall@__inference_signature_wrapper_23250") 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___21591" at "StatefulPartitionedCall@__inference_signature_wrapper_23250") 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 "c.py", line 9, in <module>
    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___21591" at "StatefulPartitionedCall@__inference_signature_wrapper_23250") 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 = ""}

saved_model_cli 명령어를 이용해서 변환하려는데 tensorrt가 안들어가면 인자가 부족하다고 하고

넣으면 libvinfer 에러가 나고... 후...

$ saved_model_cli convert --dir=. --output_dir=output --tag_set serving_default tensorrt
2021-02-02 16:52:12.317957: 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
2021-02-02 16:52:12.318003: 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.
2021-02-02 16:52:13.640651: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2021-02-02 16:52:13.640699: F tensorflow/compiler/tf2tensorrt/stub/nvinfer_stub.cc:49] getInferLibVersion symbol not found.
중지됨 (core dumped)

[링크 : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/saved_model_cli.py]

-emi-select-tf-ops=true가 flatbuffer_translate 에 전달되는 옵션인가?

[링크 : http://110.249.209.116/tiansongzhao/QT-Platform/-/blob/ffe4404132bbba3c690232c9f846ac160aa38e65/Software/resource/samples/tensorflow/tensorflow/compiler/mlir/lite/tests/mlir2flatbuffer/flex_exclusively.mlir]

MLIR (Multi-Level Intermediate Representation)

[링크 : https://mlir.llvm.org/]

결국은 돌아돌아 원점인가..

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#convert_a_model]

TFLite interpreter가 select ops 를 가지고 있는지 확인해라..

tensorflow.a 가 그럼 그 옵션을 받아야 한다는건가?

  [링크 : https://stackoverflow.com/questions/65661737/]

tensorflow lite 빌드는 묘하게 갈리는구나..

일단 static library는 bazel 도움 없이 스크립트, make로 빌드하게 되어있고

./tensorflow/lite/tools/make/download_dependencies.sh
./tensorflow/lite/tools/make/build_aarch64_lib.sh

so는 위의 스크립트로는 못하고 bazel의 도움을 받아야 빌드할 수 있는데

3단계. ARM64 바이너리 빌드하기
C 라이브러리
bazel build --config=elinux_aarch64 -c opt //tensorflow/lite/c:libtensorflowlite_c.so

C++ 라이브러리
bazel build --config=elinux_aarch64 -c opt //tensorflow/lite:libtensorflowlite.so

[링크 : https://www.tensorflow.org/lite/guide/build_arm64?hl=ko]

cross compile은 --config=를 통해서 지정이 가능하나, 특정 컴파일러를 쓰도록 지정은 어떻게 하는지 찾아봐야 겠다.

구글 페이지에는 elinux_aarch64만 보이는데 .bazelrc를 열어보니 elinux_armhf 도 존재한다(32bit?)

그리고 monolithic이 해결책일줄 알았는데.. 단일 so를 만드는 옵션일뿐.. select_tf_ops 랑은 상관이 없나보다..

# Embedded Linux options (experimental and only tested with TFLite build yet)
#     elinux:          General Embedded Linux options shared by all flavors.
#     elinux_aarch64:  Embedded Linux options for aarch64 (ARM64) CPU support.
#     elinux_armhf:    Embedded Linux options for armhf (ARMv7) CPU support.

# Other build options:
#     short_logs:       Only log errors during build, skip warnings.
#     verbose_logs:     Show all compiler warnings during build.
#     monolithic:       Build all TF C++ code into a single shared object.
#     dynamic_kernels:  Try to link all kernels dynamically (experimental).
#     libc++:           Link against libc++ instead of stdlibc++

IWDG - Independent Watchdog

Note: The RTC, the IWDG, and the corresponding clock sources are not stopped by entering Stop or Standby mode.

클럭관련

High/Low speed External/Internal

HSE = high-speed external clock signal

HSI = high-speed internal clock signal

LSI = low-speed internal clock signal

LSE = low-speed external clock signal

APB1은 36MHz 클럭 최대, APB2는 72MHz 최대

다만 타이머쪽은 둘다 72MHz를 넣을 수 있다.

USART 번호가 없어서 모르겠지만 4.5Mbit/s 혹은 2.25Mbit/s 까지 설정이 가능하다는데

아니 USART를 Mbps 급으로 쓰는데가 있긴 한건가? ㄷㄷ

아 맞다.. 있긴 있었지.. -_-

2021/01/08 - [embeded] - orange pi r1+

윈도우에서는 winscard.h 와 dll을 통해서 지원이 가능한데

리눅스에서는 개인이 만든 라이브러리가 있는 듯?

pcscd 버전을 지원을 위해 적으라는 것을 봐서는

독립적으로 작동하진 않고 pcscd 데몬을 통해 통신을 하도록 구현 된 것으로 보인다.

[링크 : https://pcsclite.apdu.fr/]