계속 tflite_convert 만 사용했는데 export_Tflite_graph_tf2.py 라는 다른 녀석을 발견했다.

ssd mobilenetv2를 주로 사용했었으니 이걸로 tflite 변환이 잘되면 좋겠네 ㅠㅠ

 

NOTE: This only supports SSD meta-architectures for now.

[링크 : http://github.com/tensorflow/models/blob/master/research/object_detection/export_tflite_graph_tf2.py]

[링크 : http://github.com/tensorflow/models/issues/9371]

 

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flex ops라는 말이 많이 나와서 도대체 멀까 정의를 찾아보는데 공식적인 문서는 발견하지 못했지만..

select 텐서플로우 ops를 텐서플로우 라이트에서 사용하는 내부 코드의 명칭

이라고 하니 더 이해가 안되네?

 

"Flex" is the internal code name for the "Using TensorFlow Lite with select TensorFlow ops" feature.

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

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

 

+

[링크 : https://github.com/tensorflow/tensorflow/issues/45875]

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$ python3
Python 3.8.5 (default, Jul 28 2020, 12:59:40) 
[GCC 9.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

 

$ pip3 install tensorflow==1.15.5
ERROR: Could not find a version that satisfies the requirement tensorflow==1.15.5 (from versions: 2.2.0rc1, 2.2.0rc2, 2.2.0rc3, 2.2.0rc4, 2.2.0, 2.2.1, 2.2.2, 2.3.0rc0, 2.3.0rc1, 2.3.0rc2, 2.3.0, 2.3.1, 2.3.2, 2.4.0rc0, 2.4.0rc1, 2.4.0rc2, 2.4.0rc3, 2.4.0rc4, 2.4.0, 2.4.1)
ERROR: No matching distribution found for tensorflow==1.15.5

[링크 : https://pypi.org/project/tensorflow/1.15.5/]

 

[링크 : https://stackoverflow.com/questions/61491893/i-cannot-install-tensorflow-version-1-15-through-pip]

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재생하려니 안되고...

Pixel format yuv420p is not supported, use bgr24

 

pix_fmt 로 바꾸려고 하니 안되네.. -_-

Incompatible pixel format 'bgr24' for codec 'mpeg4', auto-selecting format 'yuv420p'

 

오잉?

-pix_fmts           show available pixel formats
-sample_fmts        show available audio sample formats
-f fmt              force format

 

 

+

하드웨어 가속을 못 받아서 그런가 무지 힘들어 하네

 

$ ffmpeg -re -i INPUT -c:v rawvideo -pix_fmt bgra -f fbdev /dev/fb0

[swscaler @ 0x2020050] No accelerated colorspace conversion found from yuv420p to rgb24.

[링크 : https://ffmpeg.org/ffmpeg-devices.html#Examples-12]

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아주 에러가 에러에 에러를 무는구만...

 

ModuleNotFoundError: No module named 'official'

pip3 install tf-models-official

[링크 : https://github.com/tensorflow/models/issues/8291]

 

from object_detection.protos import string_int_label_map_pb2 ImportError: cannot import name 'string_int_label_map_pb2' 

sudo apt-get install protobuf-compiler

protoc object_detection/protos/*.proto --python_out=.

[링크 : https://github.com/tensorflow/models/issues/7877]

 

tensorflow.python.framework.errors_impl.NotFoundError: Unsuccessful TensorSliceReader constructor: Failed to find any matching files for checkpoint/mobilenet_v2.ckpt-1

[링크 : https://github.com/bourdakos1/Custom-Object-Detection/issues/11]

[링크 : https://github.com/tensorflow/models/issues/2676]

 

일단은 기존의 체크포인트가 있는건가 싶어서 아래의 경로에서 학습된걸 받고

config에서 300 300인걸 320 320으로 수정하고 아래와 같이 ckpt-0.data 식으로 되어 있어서 ckpt-0만 입력해주니 일단 넘어간다.

train_config: {
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint: "checkpoint/ckpt-0"
  fine_tune_checkpoint_type: "classification"

[링크 : http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_320x320_coco17_tpu-8.tar.gz]

 

tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 629145600 exceeds 10% of free system memory.

config 파일에서 찾아보니 train_config에 batch_size가 좀 큰 듯?

train_config: {
  fine_tune_checkpoint_version: V2
  fine_tune_checkpoint: "aimd/checkpoint/ckpt-0"
  fine_tune_checkpoint_type: "classification"
  batch_size: 512

[링크 : https://stackoverflow.com/questions/50304156]

 

AssertionError: Some Python objects were not bound to checkpointed values, likely due to changes in the Python program: [SyncOnReadVariable:{
  0: <tf.Variable 'block_6_expand_BN/moving_variance:0' shape=(192,) dtype=float32, numpy=

아니. 제공된 건데 왜 detection이 아니고 classification으로 되어 있는거야?

# fine_tune_checkpoint_type: "classification"

fine_tune_checkpoint_type: "detection"

[링크 : https://stackoverflow.com/questions/63552169]

 

 

ValueError: Cannot assign to variable BoxPredictor/ConvolutionalClassHead_0/ClassPredictor/bias:0 due to variable shape (6,) and value shape (273,) are incompatible

원래 학습된게 90개를 구분하도록 된 녀석인데 강제로 숫자를 바꾸어 주었더니(90->1) 저렇게 에러가 난듯.

대충 (1+1)*3 과 (90+1)*3 을 해보면 맞는 수치인 것 같은데..

[링크 : https://eehoeskrap.tistory.com/368]

 

i5-2520m 에서 1번의 train/ 1번의 eval을 하도록 설정하였는데

train 880 여개 eval 220  여개 인데 나름 1분 30초면 빠른건가? (쌩 cpu 만으로 하는데)

2021. 02. 20. (토) 23:47:07 KST
real 1m29.988s
user 2m4.538s
sys 0m6.553s
2021. 02. 20. (토) 23:48:37 KST
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data - *.record (바이너리 타입의 이미지에 대한 ROI 좌표)

training - *.pbxt (json 타입의 이미지 라벨)

images - test (eval용 이미지)

images - train (학습용 이미지)

object_detection - *.py (library)

 

generate_tfrecord.py

model_main_tf2.py (학습)

exporter_main_v2.py (pb로 결과물 내보내기)

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텐서플로우 object detection 관련 시작 지점으로 보면 되려나?

 

[링크 : http://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2.md]

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작동 여부는 확인 못함

다만.. debugutilsbad 아래꺼라 정상작동은 보증 못하려나?

 

[링크 : http://gstreamer.freedesktop.org/documentation/debugutilsbad/fpsdisplaysink.html]

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텐서플로우 모델에서 메타데이터를 보다보니 에러가 발생하는 녀석도 있어서

찾아는 보는데 도대체 먼지 알 수가 없네..

 

[링크 : http://https://www.tensorflow.org/tfx/serving/serving_basic]

[링크 : https://stackoverflow.com/questions/58927662/]

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+ 2021.02.16

오는길에 다시 보니 tensorflow model garden / research / object detection 에서 구현된

내용들이지 엄밀하게는 tensorflow 자체의 구현은 아니다.

tensorflow를 가지고 구현한 내용이라고 해야하려나?

-

 

model ssd

type ssd_mobilenet_v2_keras 를

ssd_mobilenet_v2 로 바꾸었더니 아래와 같은 에러가 발생했다.

 

INFO:tensorflow:Maybe overwriting train_steps: 1
I0212 20:50:37.009305 140651210348352 config_util.py:552] Maybe overwriting train_steps: 1
INFO:tensorflow:Maybe overwriting use_bfloat16: False
I0212 20:50:37.009468 140651210348352 config_util.py:552] Maybe overwriting use_bfloat16: False
Traceback (most recent call last):
  File "model_main_tf2.py", line 113, in <module>
    tf.compat.v1.app.run()
  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 "model_main_tf2.py", line 104, in main
    model_lib_v2.train_loop(
  File "/home/minimonk/src/SSD-MobileNet-TF/object_detection/model_lib_v2.py", line 507, in train_loop
    detection_model = MODEL_BUILD_UTIL_MAP['detection_model_fn_base'](
  File "/home/minimonk/src/SSD-MobileNet-TF/object_detection/builders/model_builder.py", line 1106, in build
    return build_func(getattr(model_config, meta_architecture), is_training,
  File "/home/minimonk/src/SSD-MobileNet-TF/object_detection/builders/model_builder.py", line 377, in _build_ssd_model
    _check_feature_extractor_exists(ssd_config.feature_extractor.type)
  File "/home/minimonk/src/SSD-MobileNet-TF/object_detection/builders/model_builder.py", line 249, in _check_feature_extractor_exists
    raise ValueError('{} is not supported. See `model_builder.py` for features '
ValueError: ssd_mobilenet_v2 is not supported. See `model_builder.py` for features extractors compatible with different versions of Tensorflow

 

model_builder.py를 열어보라는데 여러개 파일이 나타난다.?

$ sudo find / -name model_builder.py
/home/minimonk/src/SSD-MobileNet-TF/object_detection/builders/model_builder.py
/home/minimonk/src/SSD-MobileNet-TF/models/research/object_detection/builders/model_builder.py
/home/minimonk/src/SSD-MobileNet-TF/models/research/lstm_object_detection/model_builder.py
/home/minimonk/src/SSD-MobileNet-TF/build/lib/object_detection/builders/model_builder.py

 

lstm 어쩌구를 제외하면 용량이 동일하니 같은 파일로 간주하고 하나를 열어보니 다음과 같이 나오는데..

if tf_version.is_tf2() 에 의해서 사용가능한 녀석은.. 

ssd_mobilenet_v2_fpn_keras 와

ssd_mobilenet_v2_keras 뿐이다 -_-

기대했던 ssd_mobilenet_v2는  tf1 ㅠㅠ

$ vi /home/minimonk/src/SSD-MobileNet-TF/object_detection/builders/model_builder.py
if tf_version.is_tf2():
  from object_detection.models import center_net_hourglass_feature_extractor
  from object_detection.models import center_net_mobilenet_v2_feature_extractor
  from object_detection.models import center_net_mobilenet_v2_fpn_feature_extractor
  from object_detection.models import center_net_resnet_feature_extractor
  from object_detection.models import center_net_resnet_v1_fpn_feature_extractor
  from object_detection.models import faster_rcnn_inception_resnet_v2_keras_feature_extractor as frcnn_inc_res_keras
  from object_detection.models import faster_rcnn_resnet_keras_feature_extractor as frcnn_resnet_keras
  from object_detection.models import ssd_resnet_v1_fpn_keras_feature_extractor as ssd_resnet_v1_fpn_keras
  from object_detection.models import faster_rcnn_resnet_v1_fpn_keras_feature_extractor as frcnn_resnet_fpn_keras
  from object_detection.models.ssd_mobilenet_v1_fpn_keras_feature_extractor import SSDMobileNetV1FpnKerasFeatureExtractor
  from object_detection.models.ssd_mobilenet_v1_keras_feature_extractor import SSDMobileNetV1KerasFeatureExtractor
  from object_detection.models.ssd_mobilenet_v2_fpn_keras_feature_extractor import SSDMobileNetV2FpnKerasFeatureExtractor
  from object_detection.models.ssd_mobilenet_v2_keras_feature_extractor import SSDMobileNetV2KerasFeatureExtractor
  from object_detection.predictors import rfcn_keras_box_predictor
  if sys.version_info[0] >= 3:
    from object_detection.models import ssd_efficientnet_bifpn_feature_extractor as ssd_efficientnet_bifpn

if tf_version.is_tf1():
  from object_detection.models import faster_rcnn_inception_resnet_v2_feature_extractor as frcnn_inc_res
  from object_detection.models import faster_rcnn_inception_v2_feature_extractor as frcnn_inc_v2
  from object_detection.models import faster_rcnn_nas_feature_extractor as frcnn_nas
  from object_detection.models import faster_rcnn_pnas_feature_extractor as frcnn_pnas
  from object_detection.models import faster_rcnn_resnet_v1_feature_extractor as frcnn_resnet_v1
  from object_detection.models import ssd_resnet_v1_fpn_feature_extractor as ssd_resnet_v1_fpn
  from object_detection.models import ssd_resnet_v1_ppn_feature_extractor as ssd_resnet_v1_ppn
  from object_detection.models.embedded_ssd_mobilenet_v1_feature_extractor import EmbeddedSSDMobileNetV1FeatureExtractor
  from object_detection.models.ssd_inception_v2_feature_extractor import SSDInceptionV2FeatureExtractor
  from object_detection.models.ssd_mobilenet_v2_fpn_feature_extractor import SSDMobileNetV2FpnFeatureExtractor
  from object_detection.models.ssd_mobilenet_v2_mnasfpn_feature_extractor import SSDMobileNetV2MnasFPNFeatureExtractor
  from object_detection.models.ssd_inception_v3_feature_extractor import SSDInceptionV3FeatureExtractor
  from object_detection.models.ssd_mobilenet_edgetpu_feature_extractor import SSDMobileNetEdgeTPUFeatureExtractor
  from object_detection.models.ssd_mobilenet_v1_feature_extractor import SSDMobileNetV1FeatureExtractor
  from object_detection.models.ssd_mobilenet_v1_fpn_feature_extractor import SSDMobileNetV1FpnFeatureExtractor
  from object_detection.models.ssd_mobilenet_v1_ppn_feature_extractor import SSDMobileNetV1PpnFeatureExtractor
  from object_detection.models.ssd_mobilenet_v2_feature_extractor import SSDMobileNetV2FeatureExtractor
  from object_detection.models.ssd_mobilenet_v3_feature_extractor import SSDMobileNetV3LargeFeatureExtractor
  from object_detection.models.ssd_mobilenet_v3_feature_extractor import SSDMobileNetV3SmallFeatureExtractor
  from object_detection.models.ssd_mobiledet_feature_extractor import SSDMobileDetCPUFeatureExtractor
  from object_detection.models.ssd_mobiledet_feature_extractor import SSDMobileDetDSPFeatureExtractor
  from object_detection.models.ssd_mobiledet_feature_extractor import SSDMobileDetEdgeTPUFeatureExtractor
  from object_detection.models.ssd_mobiledet_feature_extractor import SSDMobileDetGPUFeatureExtractor
  from object_detection.models.ssd_pnasnet_feature_extractor import SSDPNASNetFeatureExtractor
  from object_detection.predictors import rfcn_box_predictor

 

[링크 : https://stackoverflow.com/questions/65938445/]

 

+

와.. ssd_mobilenet_v2_fpn_keras를 돌리는데 메모리 부족으로 죽어버리네 ㄷㄷ

눈에 보이는건.. additional_layer_depth 인가.. 이걸 줄이고 해봐야 겠네..

    feature_extractor {
      type: 'ssd_mobilenet_v2_fpn_keras'
      use_depthwise: true
      fpn {
        min_level: 3
        max_level: 7
        additional_layer_depth: 128
      }
      min_depth: 16
      depth_multiplier: 1.0
      conv_hyperparams {
        activation: RELU_6,
        regularizer {
          l2_regularizer {
            weight: 0.00004
          }
        }
        initializer {
          random_normal_initializer {
            stddev: 0.01
            mean: 0.0
          }
        }
        batch_norm {
          scale: true,
          decay: 0.997,
          epsilon: 0.001,
        }
      }
      override_base_feature_extractor_hyperparams: true
    }

 

/home/minimonk/.local/lib/python3.8/site-packages/tensorflow/python/keras/backend.py:434: UserWarning: `tf.keras.backend.set_learning_phase` is deprecated and will be removed after 2020-10-11. To update it, simply pass a True/False value to the `training` argument of the `__call__` method of your layer or model.
  warnings.warn('`tf.keras.backend.set_learning_phase` is deprecated and '
2021-02-12 21:23:47.163320: W tensorflow/core/framework/cpu_allocator_impl.cc:80] Allocation of 1258291200 exceeds 10% of free system memory.
죽었음

 

+

depth를 줄이고 해보니 되는척 하다가 또 에러가 발생 ㅋㅋ

    ValueError: Number of feature maps is expected to equal the length of `num_anchors_per_location`.

 

되는 척 하더니 안되네? ㅠㅠ

AssertionError: Some Python objects were not bound to checkpointed values, likely due to changes in the Python program: [MirroredVariable:{
  0: <tf.Variable 'block_8_depthwise/depthwise_kernel:0' shape=(3, 3, 384, 1) dtype=float32, numpy=

...

WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.
W0212 21:37:27.939997 140162079770432 util.py:168] A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/guide/checkpoint#loading_mechanics for details.

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