'프로그램 사용'에 해당되는 글 1813건

  1. 2021.05.01 tflite type
  2. 2021.04.30 rpi distcc with ccache 실패 ㅠㅠ
  3. 2021.04.28 rpi distcc 성공인데 실패
  4. 2021.04.19 tflite example
  5. 2021.04.16 tflite convert
  6. 2021.04.16 LSTM - Long short-term memory
  7. 2021.04.15 quantization: 0.003921568859368563 * q
  8. 2021.04.14 tflite_converter quantization
  9. 2021.04.14 tensorboard graph
  10. 2021.04.13 generate_tfrecord.py

 

변수 추적해보니 그게 그거인가?

  int output = interpreter->outputs()[0];
  TfLiteIntArray* output_dims = interpreter->tensor(output)->dims;
  // assume output dims to be something like (1, 1, ... ,size)
  auto output_size = output_dims->data[output_dims->size - 1];

 

    const float* detection_locations = interpreter->tensor(interpreter->outputs()[0])->data.f;
    const float* detection_classes=interpreter->tensor(interpreter->outputs()[1])->data.f;
    const float* detection_scores = interpreter->tensor(interpreter->outputs()[2])->data.f;
    const int    num_detections = *interpreter->tensor(interpreter->outputs()[3])->data.f;

    //there are ALWAYS 10 detections no matter how many objects are detectable
    //cout << "number of detections: " << num_detections << "\n";

    const float confidence_threshold = 0.5;
    for(int i = 0; i < num_detections; i++){
        if(detection_scores[i] > confidence_threshold){
            int  det_index = (int)detection_classes[i]+1;
            float y1=detection_locations[4*i  ]*cam_height;
            float x1=detection_locations[4*i+1]*cam_width;
            float y2=detection_locations[4*i+2]*cam_height;
            float x2=detection_locations[4*i+3]*cam_width;

            Rect rec((int)x1, (int)y1, (int)(x2 - x1), (int)(y2 - y1));
            rectangle(src,rec, Scalar(0, 0, 255), 1, 8, 0);
            putText(src, format("%s", Labels[det_index].c_str()), Point(x1, y1-5) ,FONT_HERSHEY_SIMPLEX,0.5, Scalar(0, 0, 255), 1, 8, 0);
        }
    }

 

 

typedef struct {
  int size;
#if !defined(__clang__) && defined(__GNUC__) && __GNUC__ == 6 && \
    __GNUC_MINOR__ >= 1
  int data[0];
#else
  int data[];
#endif
} TfLiteIntArray;

typedef union {
  int* i32;
  int64_t* i64;
  float* f;
  char* raw;
  const char* raw_const;
  uint8_t* uint8;
  bool* b;
  int16_t* i16;
  TfLiteComplex64* c64;
  int8_t* int8;
} TfLitePtrUnion;

typedef struct {
  TfLiteType type;
  TfLitePtrUnion data;
  TfLiteIntArray* dims;
  TfLiteQuantizationParams params;
  TfLiteAllocationType allocation_type;
  size_t bytes;
  const void* allocation;
  const char* name;
  TfLiteDelegate* delegate;
  TfLiteBufferHandle buffer_handle;
  bool data_is_stale;
  bool is_variable;
  TfLiteQuantization quantization;
} TfLiteTensor;

[링크 : https://android.googlesource.com/platform/external/tensorflow/.../tensorflow/lite/c/c_api_internal.h]

 

현재 소스에서는 common.h 로 옮겨진듯

[링크 : https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/c/common.h]

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그런 이유로 distcc-pump 모드 시도 ㅠㅠ

 

[링크 : https://itmir.tistory.com/454]

[링크 : https://www.whatwant.com/entry/Ubuntu에서-ccache-사용하기]

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distcc 패키지 설치하고, tensorflow lite 빌드 시도

원래는 30분 정도 걸렸는데 (rpi 3b, 4core 기준) 얼마나 줄어들려나?

(느낌으로는 SD 메모리라 disk io로 인해 오히려 더 느려질지도 모르겠다는 불안감이..)

 

접속이 안되는 것 같아서 다른 문서들을 자세히 보니 설정을 제대로 안했네!

distcc[946] (dcc_build_somewhere) Warning: failed to distribute, running locally instead
distcc[946] (dcc_parse_hosts) Warning: /home/pi/.distcc/zeroconf/hosts contained no hosts; can't distribute work
distcc[946] (dcc_zeroconf_add_hosts) CRITICAL! failed to parse host file.

 

/etc/default/ditscc 파일에서 allow와 listener를 수정해주고 service distcc restart 하면 끝!

$ cat /etc/default/distcc
# Defaults for distcc initscript
# sourced by /etc/init.d/distcc

#
# should distcc be started on boot?
#
 STARTDISTCC="true"

#STARTDISTCC="false"

#
# Which networks/hosts should be allowed to connect to the daemon?
# You can list multiple hosts/networks separated by spaces.
# Networks have to be in CIDR notation, e.g. 192.168.1.0/24
# Hosts are represented by a single IP address
#
# ALLOWEDNETS="127.0.0.1"

ALLOWEDNETS="127.0.0.1 192.168.0.0/16"

#
# Which interface should distccd listen on?
# You can specify a single interface, identified by it's IP address, here.
#
# LISTENER="127.0.0.1"

LISTENER=""

#
# You can specify a (positive) nice level for the distcc process here
#
# NICE="10"

NICE="10"

#
# You can specify a maximum number of jobs, the server will accept concurrently
#
# JOBS=""

JOBS=""

#
# Enable Zeroconf support?
# If enabled, distccd will register via mDNS/DNS-SD.
# It can then automatically be found by zeroconf enabled distcc clients
# without the need of a manually configured host list.
#
 ZEROCONF="true"

#ZEROCONF="false"

 

MAKEFLAGS에 CC=/usr/lib/distcc/gcc 이 포인트 이긴 한데..

tensorflow/tensorflow/lite/tools/make $ cat ./build_rpi_lib.sh
#!/bin/bash
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

set -x
set -e

SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
TENSORFLOW_DIR="${SCRIPT_DIR}/../../../.."

FREE_MEM="$(free -m | awk '/^Mem/ {print $2}')"
# Use "-j 4" only memory is larger than 2GB
if [[ "FREE_MEM" -gt "2000" ]]; then
  NO_JOB=4
else
  NO_JOB=1
fi

export MAKEFLAGS="CXX=/usr/lib/distcc/g++ CC=/usr/lib/distcc/gcc"
make -j 8 TARGET=rpi -C "${TENSORFLOW_DIR}" -f tensorflow/lite/tools/make/Makefile $@
#make -j ${NO_JOB} CC=/usr/lib/distcc/gcc TARGET=rpi -C "${TENSORFLOW_DIR}" -f tensorflow/lite/tools/make/Makefile $@

 

/etc/distcc/hosts 에 사용할 노드 이름을 넣으면 되는데 자기 자신이 들어가지 않으면

distcc 에서는 슬레이브 노드들로만 빌드를 하게 된다.

# As described in the distcc manpage, this file can be used for a global
# list of available distcc hosts.
#
# The list from this file will only be used, if neither the
# environment variable DISTCC_HOSTS, nor the file $HOME/.distcc/hosts
# contains a valid list of hosts.
#
# Add a list of hostnames in one line, seperated by spaces, here.
#
tf2
tf3
+zeroconf

 

가끔 이런거 나오는데 그냥 무시하면 zeroconf에 의해서 붙는지 슬레이브 노드(?) 쪽 cpu를 빨아먹긴 한다.

distcc[1323] (dcc_build_somewhere) Warning: failed to distribute, running locally instead
distcc[1332] (dcc_build_somewhere) Warning: failed to distribute, running locally instead

 

[링크 : http://openframeworks.cc/ko/setup/raspberrypi/raspberry-pi-distcc-guide/]

[링크 : http://jtanx.github.io/2019/04/19/rpi-distcc-node/]

 

+

/var/log/distcc.log를 보는데

정상적으로 잘되면 COMPILE_OK가 뜨지만

어느순간 갑자기 client fd disconnected가 뜨면서 빌드가 멈춘다.

근데 time:305000ms 정도 대충 5분 timewait 걸리는것 같아서

오히려 안하니만 못한 상황..

distccd[14090] (dcc_job_summary) client: 192.168.52.209:40940 COMPILE_OK exit:0 sig:0 core:0 ret:0 time:16693ms g++ tensorflow/lite/kernels/cpu_backend_gemm_eigen.cc
distccd[14091] (dcc_collect_child) ERROR: Client fd disconnected, killing job
distccd[14091] (dcc_writex) ERROR: failed to write: Broken pipe
distccd[14091] (dcc_job_summary) client: 192.168.52.209:40932 CLI_DISCONN exit:107 sig:0 core:0 ret:107 time:307172ms

 

아무튼 위와 같은 에러를 내며 뻗을때 개별 노드에서는 이런식으로 IO가 미쳐 날뛴다.

--total-cpu-usage-- -dsk/total- -net/total- ---paging-- ---system--
usr sys idl wai stl| read  writ| recv  send|  in   out | int   csw

  5   2  10  83   0| 928k 4048k|1063B  252B|  68k 2040k|1830  3320
  0   3  27  69   0|7840M   27M|2919k   73k|1512k   11M| 245k  402k missed 238 ticks
  2   1   0  97   0| 176k    0 |   0     0 |8192B    0 |  19    23  missed 2 ticks

 

+

cpp,lzo를 넣어서 해볼까?

[링크 : https://wiki.gentoo.org/wiki/Distcc/ko]

 

+

export MAKEFLAGS="CXX=/usr/lib/distcc/g++ CC=/usr/lib/distcc/gcc"
#export MAKEFLAGS="CXX=/usr/bin/distcc-pump CC=/usr/bin/distcc-pump"
make -j 8 TARGET=rpi -C "${TENSORFLOW_DIR}" -f tensorflow/lite/tools/make/Makefile $@
#make -j ${NO_JOB} CC=/usr/lib/distcc/gcc TARGET=rpi -C "${TENSORFLOW_DIR}" -f tensorflow/lite/tools/make/Makefile $@

 

되는데 pump가 아닌거랑 동일하게 io가 폭주해서 뻗는건 동일하다.

$ distcc-pump ./build_rpi_lib.sh

 

+

distccmon-text 는 slave node가 아니라 server node에서 해야 하는구나..

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[링크 : https://github.com/EdjeElectronics/TensorFlow-Lite-Object-Detection-on-Android-and-Raspberry-Pi/blob/master/TFLite_detection_image.py]

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[링크 : http://www.tensorflow.org/lite/api_docs/python/tf/lite/Optimize]

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

 

[링크 : http://medium.com/sclable/model-quantization-using-tensorflow-lite-2fe6a171a90d]

 

[링크 : http://www.tensorflow.org/lite/performance/quantization_spec]

[링크 : http://www.tensorflow.org/api_docs/python/tf/lite/TFLiteConverter]

 

 

 

 

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tensorflow model 뒤져보다 보니 lstm 이라는 용어는 본적이 있는데

귀찮아서 넘기다가 이번에도 또 검색중에 걸려나와서 조사.

 

RNN(Recurrent nerural network) 에서 사용하는 기법(?)으로 문맥을 강화해주는 역활을 하는 듯.

 

[링크 : http://euzl.github.io/hackday_1/]

[링크 : https://en.wikipedia.org/wiki/Long_short-term_memory]

 

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tflite로 변환시 unit8로 양자화 하면

분명 범위는 random으로 들어가야 해서 quantization 범위가 조금은 달라질 것으로 예상을 했는데

항상 동일한 0.003921568859368563 * q로 나와 해당 숫자로 검색을 하니

0~255 범위를 float로 정규화 하면 해당 숫자가 나온다고..

 

0.00392 * 255 = 0.9996 이 나오긴 하네?

quantization of input tensor will be close to (0.003921568859368563, 0). mean is the integer value from 0 to 255 that maps to floating point 0.0f. std_dev is 255 / (float_max - float_min). This will fix one possible problem

[링크 : https://stackoverflow.com/questions/54830869/]

[링크 : https://github.com/majidghafouri/Object-Recognition-tf-lite/issues/1]

 

+

output_format: Output file format. Currently must be {TFLITE, GRAPHVIZ_DOT}. (default TFLITE)
quantized_input_stats: Dict of strings representing input tensor names mapped to tuple of floats representing the mean and standard deviation of the training data (e.g., {"foo" : (0., 1.)}). Only need if inference_input_type is QUANTIZED_UINT8. real_input_value = (quantized_input_value - mean_value) / std_dev_value. (default {})
default_ranges_stats: Tuple of integers representing (min, max) range values for all arrays without a specified range. Intended for experimenting with quantization via "dummy quantization". (default None)
post_training_quantize: Boolean indicating whether to quantize the weights of the converted float model. Model size will be reduced and there will be latency improvements (at the cost of accuracy). (default False)

[링크 : http://man.hubwiz.com/.../python/tf/lite/TFLiteConverter.html]

 

TOCO(Tensorflow Lite Optimized Converter)

[링크 : https://junimnjw.github.io/%EA%B0%9C%EB%B0%9C/2019/08/09/tensorflow-lite-2.html]

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이것저것.. 원본 소스까지 뒤지고 있는데 이렇다 할 원하는 답이 안보인다.

[링크 : https://www.tensorflow.org/model_optimization/guide/quantization/training]

[링크 : https://www.tensorflow.org/model_optimization/guide/quantization/training_example]

[링크 : https://github.com/tensorflow/.../lite/g3doc/performance/post_training_quantization.md]

[링크 : https://github.com/tensorflow/.../lite/g3doc/performance/quantization_spec.md]

 

util_test.py

def _generate_integer_tflite_model(quantization_type=dtypes.int8):
  """Define an integer post-training quantized tflite model."""
  # Load MNIST dataset
  n = 10  # Number of samples
  (train_images, train_labels), (test_images, test_labels) = \
      tf.keras.datasets.mnist.load_data()
  train_images, train_labels, test_images, test_labels = \
      train_images[:n], train_labels[:n], test_images[:n], test_labels[:n]

  # Normalize the input image so that each pixel value is between 0 to 1.
  train_images = train_images / 255.0
  test_images = test_images / 255.0

  # Define TF model
  model = tf.keras.Sequential([
      tf.keras.layers.InputLayer(input_shape=(28, 28)),
      tf.keras.layers.Reshape(target_shape=(28, 28, 1)),
      tf.keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation="relu"),
      tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),
      tf.keras.layers.Flatten(),
      tf.keras.layers.Dense(10)
  ])

  # Train
  model.compile(
      optimizer="adam",
      loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
      metrics=["accuracy"])

  model.fit(
      train_images,
      train_labels,
      epochs=1,
      validation_split=0.1,
  )

  # Convert TF Model to an Integer Quantized TFLite Model
  converter = tf.lite.TFLiteConverter.from_keras_model(model)
  converter.optimizations = {tf.lite.Optimize.DEFAULT}
  def representative_dataset_gen():
    for _ in range(2):
      yield [
          np.random.uniform(low=0, high=1, size=(1, 28, 28)).astype(
              np.float32)
      ]
  converter.representative_dataset = representative_dataset_gen
  if quantization_type == dtypes.int8:
    converter.target_spec.supported_ops = {tf.lite.OpsSet.TFLITE_BUILTINS_INT8}
  else:
    converter.target_spec.supported_ops = {
        tf.lite.OpsSet
        .EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8
    }
  tflite_model = converter.convert()

  return tflite_model

 

lite_v2_test.py

  def _getIntegerQuantizeModel(self):
    np.random.seed(0)

    root = tracking.AutoTrackable()

    @tf.function(
        input_signature=[tf.TensorSpec(shape=[1, 5, 5, 3], dtype=tf.float32)])
    def func(inp):
      conv = tf.nn.conv2d(
          inp, tf.ones([3, 3, 3, 16]), strides=[1, 1, 1, 1], padding='SAME')
      output = tf.nn.relu(conv, name='output')
      return output

    def calibration_gen():
      for _ in range(5):
        yield [np.random.uniform(-1, 1, size=(1, 5, 5, 3)).astype(np.float32)]

    root.f = func
    to_save = root.f.get_concrete_function()
    return (to_save, calibration_gen)


 def testInvalidIntegerQuantization(self, is_int16_quantize,
                                     inference_input_output_type):
    func, calibration_gen = self._getIntegerQuantizeModel()

    # Convert quantized model.
    quantized_converter = lite.TFLiteConverterV2.from_concrete_functions([func])
    quantized_converter.optimizations = [lite.Optimize.DEFAULT]
    quantized_converter.representative_dataset = calibration_gen
    if is_int16_quantize:
      quantized_converter.target_spec.supported_ops = [
          lite.OpsSet.\
          EXPERIMENTAL_TFLITE_BUILTINS_ACTIVATIONS_INT16_WEIGHTS_INT8,
          lite.OpsSet.TFLITE_BUILTINS
      ]
    with self.assertRaises(ValueError) as error:
      quantized_converter.inference_input_type = dtypes.int8
      quantized_converter.inference_output_type = dtypes.int8
      quantized_converter.convert()
    self.assertEqual(
        'The inference_input_type and inference_output_type '
        "must be in ['tf.float32', 'tf.int16'].", str(error.exception))


  def testCalibrateAndQuantizeBuiltinInt16(self):
    func, calibration_gen = self._getIntegerQuantizeModel()

    # Convert float model.
    float_converter = lite.TFLiteConverterV2.from_concrete_functions([func])
    float_tflite_model = float_converter.convert()
    self.assertIsNotNone(float_tflite_model)

    converter = lite.TFLiteConverterV2.from_concrete_functions([func])
    # TODO(b/156309549): We should add INT16 to the builtin types.
    converter.optimizations = [lite.Optimize.DEFAULT]
    converter.target_spec.supported_ops = [lite.OpsSet.TFLITE_BUILTINS_INT8]
    converter.representative_dataset = calibration_gen
    converter._experimental_calibrate_only = True
    calibrated_tflite = converter.convert()
    quantized_tflite_model = mlir_quantize(
        calibrated_tflite, inference_type=_types_pb2.QUANTIZED_INT16)

    self.assertIsNotNone(quantized_tflite_model)

    # The default input and output types should be float.
    interpreter = Interpreter(model_content=quantized_tflite_model)
    interpreter.allocate_tensors()
    input_details = interpreter.get_input_details()
    self.assertLen(input_details, 1)
    self.assertEqual(np.float32, input_details[0]['dtype'])
    output_details = interpreter.get_output_details()
    self.assertLen(output_details, 1)
    self.assertEqual(np.float32, output_details[0]['dtype'])

    # Ensure that the quantized weights tflite model is smaller.
    self.assertLess(len(quantized_tflite_model), len(float_tflite_model))

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pb 파일을 tensorboard에 끌어가면

간혹(?) graph 항목에 내용이 없는 경우가 있어서

어떻게 해야 해당 항목을 활성화 할 수 있나 검색중

 

[링크 : http://stackoverflow.com/questions/48391075]

 

writer = tf.summary.FileWriter("output", sess.graph)

[링크 : http://www.h2kinfosys.com/blog/tensorboard-how-to-use-tensorboard-for-graph-visualization/]

[링크 : http://www.tensorflow.org/tensorboard/graphs]

 

 

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먼가 이상해서 하나하나 뜯어 보는중

[링크 : https://www.tensorflow.org/tutorials/load_data/tfrecord]

[링크 : https://www.kaggle.com/gauravchopracg/understanding-tfrecord-format]

 

학습을 하는건 돌아가는데 

탐지가 안되거나 입력 범위가 이상하거나 이런 문제가 있어서 확인하는데

 

tfrecord 에서는 학습에 필요한 이미지를 읽어서 넣어 두는 듯?

그 과정에서 원본이 들어가냐 bitmpa으로 들어가냐를 확인하는데

 

혹시나 해서 1년 이내 글로 찾아보니 업그레이드 된 generate_tfrecord.py 를 발견!

 

[링크 : https://github.com/EdjeElectronics/.../issues/427]

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