投稿日：2019-11-15T15:48:41+09:00

Winsows10でゼロから機械学習環境を作ってみる

はじめに

皆さん、やってますか？

機械学習

ここ数年、第3次AIブームなんて言われて、もてはやされているアイツです。
人工知能、機械学習、ニューラルネットワーク、ディープラーニング
最近、黙々と勉強してはいますが、ん～…なんだか難しいですね…。

今回は、その辺はいったん置いておいて、機械学習の環境をサクッと作っちゃいましょうというお話です。
意外と簡単に作れちゃいます。
(参考記事はたくさんあると思いますが、自分用メモも兼ねてます）

Google Colaboratoryでいいじゃん～って方は
こちら

PC環境

・OS:Windows10 Pro
・GPU:GeForce 940MX
・CPU:Core i5-7200U
・メモリ:16GB

nVIDIA CUDA ToolkitとcuDNNのインストール

まずはこちらの記事を参考にCUDA ToolkitとcuDNNをインストールしちゃいましょう。
TensorFlow(GPU版)を使う場合に必要になるためです。

注意点としては、記事中のリンクでCUDAをダウンロードしてしまうと、最新の10.1になってしまい、TensorFlowで使う場合シンボリックリンクを設定しなければならないため、こちらから10.0のアーカイブをダンロードしてインストールした方が楽です。

Anacondaのインストール

Pythonの環境はAnacondaを使います。

実のところ一度、素のPython3.7環境でも機械学習の環境を作りました。
そして「Windows10でAnacondaなしで機械学習環境を作る」みたいな記事を書こうと思いました。

ですが、調べているうちにcondaでインストールした環境の方が速度が速いという記事をいくつか見つけたため、
「え～？じゃあAnaconndaで環境作り直す～？」
という気持ちで改めて、AnacondaでPython環境を構築しました。

ということでまずはAnacondaの公式からインストーラをダウンロードしてインストールします。

途中の
Add Anaconda to my PATH environment variavle
はチェックを入れておけば、環境変数にAnaconda系のパスが勝手に入ると思います。

インストール後は、コマンドプロンプトやPowerShell等で

conda python pip
の3つを実行しパスが通っているか確認しましょう。

仮想環境作成

AnacondaはPythonの環境をお手軽に作れてしまします。
どういう利点があるのかとか、詳しく知りたい人は今回はGoogle先生にお任せします。

仮想環境を作成するコマンドは以下
> conda create --name myenv python=3.7
myenvの部分は仮想環境の名前なのでお好きな名前にしちゃってください。
これだけで環境がバンバン作れちゃいます。

環境の切り替えは
> conda activate myenv
で切り替えられます。

※ 注意として、よく見かけるのが、condaがついていない
> activate myenv
ですが、古いバージョンの書き方のようで、
一見何もエラーがなく切り替わったように見えますが、
後述のinfoで調べても環境が切り替わっていません。

自分が今どの環境をアクティブにしているか確認するには
> conda info -e
で以下のように確認できます。
# conda environments:
#
base                     C:\Users\<ユーザー名>\Anaconda3
myenv                 *  C:\Users\<ユーザー名>\Anaconda3\envs\myenv
「*」がついているのが現在の環境です。

必要そうなパッケージのインストール

必要そうなと書いているように、もしかしたらいらないかもしれません。
いろんな機械学習を試してみたい！って場合は必要かも。
conda install pandas
conda install scikit-learn
conda install scikit-image
OpenAI Gym環境インストール
pip install gym
conda install -c conda-forge jsanimation
conda install pyglet
conda install -c conda-forge ffmpeg
pip install --no-index -f https://github.com/Kojoley/atari-py/releases atari_py
conda installとpip installを混在させると環境が壊れるという話もあるので、
できればpip installは使わないようにしたいのですが、Anacondaにパッケージがない場合はやむなし…。

KerasとTensorFlow環境インストール

TensorFlowあればKeras内包されてるし、いらないんじゃない？
という意見もあるかと思いますが、サンプルとかで混在している場合もあったりするので、念の為。
conda install -c conda-forge keras
conda install pydotplus
conda install tensorflow
conda install tensorflow-gpu
完成！

以上で、ある程度の機械学習の環境が整いました。
最終的なconda list
# Name                    Version                   Build  Channel
_tflow_select             2.1.0                       gpu
absl-py                   0.8.1                    py37_0
astor                     0.8.0                    py37_0
astroid                   2.3.2                    py37_0
atari-py                  1.2.1                    pypi_0    pypi
blas                      1.0                         mkl
ca-certificates           2019.10.16                    0
certifi                   2019.9.11                py37_0
cloudpickle               1.2.2                      py_0
colorama                  0.4.1                    py37_0
cudatoolkit               10.0.130                      0
cudnn                     7.6.4                cuda10.0_0
cycler                    0.10.0                   py37_0
cytoolz                   0.10.0           py37he774522_0
dask-core                 2.6.0                      py_0
decorator                 4.4.1                      py_0
ffmpeg                    4.2                  h6538335_0    conda-forge
freetype                  2.9.1                ha9979f8_1
future                    0.18.2                   pypi_0    pypi
gast                      0.2.2                    py37_0
google-pasta              0.1.7                      py_0
grpcio                    1.16.1           py37h351948d_1
gym                       0.15.4                   pypi_0    pypi
h5py                      2.9.0            py37h5e291fa_0
hdf5                      1.10.4               h7ebc959_0
icc_rt                    2019.0.0             h0cc432a_1
icu                       58.2                 ha66f8fd_1
imageio                   2.6.1                    py37_0
intel-openmp              2019.4                      245
isort                     4.3.21                   py37_0
joblib                    0.14.0                     py_0
jpeg                      9b                   hb83a4c4_2
jsanimation               0.1                        py_1    conda-forge
keras                     2.3.1            py37h21ff451_0    conda-forge
keras-applications        1.0.8                      py_0
keras-preprocessing       1.1.0                      py_1
kiwisolver                1.1.0            py37ha925a31_0
lazy-object-proxy         1.4.3            py37he774522_0
libgpuarray               0.7.6             hfa6e2cd_1003    conda-forge
libpng                    1.6.37               h2a8f88b_0
libprotobuf               3.9.2                h7bd577a_0
libtiff                   4.1.0                h56a325e_0
mako                      1.1.0                      py_0    conda-forge
markdown                  3.1.1                    py37_0
markupsafe                1.1.1            py37hfa6e2cd_0    conda-forge
matplotlib                3.1.1            py37hc8f65d3_0
mccabe                    0.6.1                    py37_1
mkl                       2019.4                      245
mkl-service               2.3.0            py37hb782905_0
mkl_fft                   1.0.15           py37h14836fe_0
mkl_random                1.1.0            py37h675688f_0
networkx                  2.4                        py_0
numpy                     1.17.3           py37h4ceb530_0
numpy-base                1.17.3           py37hc3f5095_0
olefile                   0.46                     py37_0
opencv-python             4.1.1.26                 pypi_0    pypi
openssl                   1.1.1d               he774522_3
opt_einsum                3.1.0                      py_0
pandas                    0.25.2           py37ha925a31_0
pillow                    6.2.1            py37hdc69c19_0
pip                       19.3.1                   py37_0
protobuf                  3.9.2            py37h33f27b4_0
pydotplus                 2.0.2                    py37_1
pyglet                    1.3.2                    pypi_0    pypi
pygpu                     0.7.6           py37hc8d92b1_1000    conda-forge
pylint                    2.4.3                    py37_0
pyparsing                 2.4.2                      py_0
pyqt                      5.9.2            py37h6538335_2
pyreadline                2.1                      py37_1
python                    3.7.5                h8c8aaf0_0
python-dateutil           2.8.1                      py_0
pytz                      2019.3                     py_0
pywavelets                1.1.1            py37he774522_0
pyyaml                    5.1.2            py37hfa6e2cd_0    conda-forge
qt                        5.9.7            vc14h73c81de_0
scikit-image              0.15.0           py37ha925a31_0
scikit-learn              0.21.3           py37h6288b17_0
scipy                     1.3.1            py37h29ff71c_0
setuptools                41.6.0                   py37_0
sip                       4.19.8           py37h6538335_0
six                       1.12.0                   py37_0
sqlite                    3.30.1               he774522_0
tensorboard               2.0.0              pyhb230dea_0
tensorflow                2.0.0           gpu_py37h57d29ca_0
tensorflow-base           2.0.0           gpu_py37h390e234_0
tensorflow-estimator      2.0.0              pyh2649769_0
tensorflow-gpu            2.0.0                h0d30ee6_0
termcolor                 1.1.0                    py37_1
theano                    1.0.4           py37h6538335_1000    conda-forge
tk                        8.6.8                hfa6e2cd_0
toolz                     0.10.0                     py_0
tornado                   6.0.3            py37he774522_0
tqdm                      4.36.1                     py_0
vc                        14.1                 h0510ff6_4
vs2015_runtime            14.16.27012          hf0eaf9b_0
vs2015_win-64             14.0.25420          h55c1224_11
webencodings              0.5.1                    py37_1
werkzeug                  0.16.0                     py_0
wheel                     0.33.6                   py37_0
wincertstore              0.2                      py37_0
wrapt                     1.11.2           py37he774522_0
xz                        5.2.4                h2fa13f4_4
yaml                      0.1.7             hfa6e2cd_1001    conda-forge
zlib                      1.2.11               h62dcd97_3
zstd                      1.3.7                h508b16e_0
まとめ

つらつらとインストールして、今は1つの仮想環境にいろいろまとめて入っていますが。
用途によって別途仮想環境を作って分けてもいいと思います。

あとは、サンプルを探して動かしてみるなり、
自分で試行錯誤してみたり、いろいろ試せるようになると思います！

・
・
・
と言いたいところですが、TensorFlow2.0.0のサンプルがまだまだ転がってない状況なんですよね…。
過去バージョンのサンプルはたくさんあるのですが、2.0.0で変わった箇所が多く、そのままでは動かないこと多数です。

TensorFlowの公式もGoogleColaboratoryでチュートリアルを体験できるようになっています。
（Googleが開発したライブラリなので当然っちゃ当然ですが）

なので、旧バージョンのサンプルを自分なりに理解して、改変できるくらいの知識はやはり必要そうですね…_(┐「ε:)_

まだまだ機械学習についてはで勉強中ですが、今後何かおもしろいことができるようになってきたら、
自分の脳のメモリが追いつく限り、発信していければなーと思っています！
~~（既にパンパン）~~

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投稿日：2019-11-15T14:05:54+09:00

TensorFlow2.0エラー

出たエラー

TypeError: len is not well defined for symbolic Tensors. (activation_3/Identity:0) Please call x.shape rather than len(x) for shape information.

解決策

$ pip install tensorflow == 2.0.0-beta

結果

多分解決　

参考にしたサイト

https://github.com/wau/keras-rl2/issues/4

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投稿日：2019-11-15T10:36:25+09:00

TypeError: The added layer must be an instance of class Layer. Found: <keras.engine.training.Model object at XXXXXXXX>

エラー内容

コード

from keras.applications.resnet50 import ResNet50
from tensorflow.keras.models import Sequential

resnet = ResNet50(include_top=False, pooling='avg', weights='imagenet')
my_new_model = Sequential()
my_new_model.add(resnet)

エラー

TypeError: The added layer must be an instance of class Layer. Found: <keras.engine.training.Model object at XXXXXXXX>

原因

kerasとtensorflow.kerasを同時に使っていたからだと思う。

解決

from tensorflow.keras.applications.resnet50 import ResNet50
from tensorflow.keras.models import Sequential

resnet = ResNet50(include_top=False, pooling='avg', weights='imagenet')
my_new_model = Sequential()
my_new_model.add(resnet)

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投稿日：2019-11-15T01:01:59+09:00

TensorFlow World Extended Tokyo 2019 @Google渋谷オフィスへ参加してきて感じたTensorFlow2.0の進化

はじめに

TensorFlow Worldの日本版に行ってきたので簡単にレポートします。

TensorFlow Worldは2019年10月28日〜31日にカリフォルニアで開催された、O'Reilly主催の初のTensorFlowに関するカンファレンスです。

今回はその日本版ということで、TensorFlow User GroupとGoogleさんの協力で開催されたようです。日本でもこういう会を設けてくれるのはとてもありがたい。
Connpass上で募集を見かけ、応募したところ参加枠108人に対し216人が応募。最初の抽選に落ちたものの、前日に再度確認すると繰り上げ当選しました。当日も2, 3割が空席のようでした。

Connpassのイベントページ
https://tfug-tokyo.connpass.com/event/152615/

Slido.comの当日質問ページ
#TFUG191114

今回は折角参加できたので、感想&簡単なメモを共有させていただきます！

感想

オープンなプロジェクトにContributeしてみたいなあ

Eager Executionって何だ？？？

気になったキーワード

Commuinty / Open Source

SIG

Mobile BERT

わからんワード

Eager Excution

RFC

What-If

以下はメモ。当日のスライドは後日運営の方がUPしてくださるらしいです。

TensorFlow のエコシステムとTensorFlow

ohtamanさん(Twitter:@ohtaman)

TensorFlow2.0の開発体制

1.xまではGoogleで開発していた。
一方、2.0は11のSIGS(Special Interest Group)で開発している。
SIGの一例として、SIG Addonsがある。2.0でディスコンになった機能をサポートするためのSIG。
Docs Translation 11/18にTensorFlowのドキュメントを翻訳しつつ勉強する会。

TensorFlow Hub

https://tfhub.dev
学習済モデルが公開されている。.pyのソースでURLを指定するだけで、学習済モデルが使える。
Google, Microsoft, Nvidiaなどのモデルが一同に会している。

TensorFlow Enterprise

従来サポートが弱かった、Tensorflowをビジネス向けに適用するためのサービスを追加する。
フレームワークではな、あくまでサービスの総称。Google Cloudでサポートするとのこと。
最大過去3年間のバグフィックスをサポートするなど手厚いので、ビジネス利用が促進されることを期待したい。

TensorFlow Lite のアップデート

Le Viet Gia Khanhさん(TensorFlow Developer Engineer at Google)

TensorFlow Liteというエッジコンピューティング用システムがある。
エッジでの機械学習は低レイテンシの実現・ネットワークが繋げられない・セキュリティセンシティブなケースで有用。
BERTという自然言語処理モデルがあることを知ったのですが非常に精度の高いモデルのようですね。

TensorFlow Lite Micro

proppyさん(Developer Programs Engineer at Google)
TFLite microというデバイスで学習データを入力、PCで学習、モデルをANSI Cなどのバイナリとして出力することができる。

TensorFlow2.0とTensorBoard.dev

TensorFlow2.0の変更点

APIの整理

Eager Executionがデフォルトに

tf.Variable()の管理方法が簡略化

Eagermodeの副作用として自作関数のパフォーマンスが低下する。tf.functionでwrapすることで最適化する。

TensorBoard

Logはtf.kerasのcallbackを用いる。
tb_cb = tf.keras.callbacks.TensorBoard(...) # これでカバーできないものもある。
model.fit(..., callbacks=[tb_cb])
GANの学習中など、デフォルトのcallback関数で機能が不足する場合は自作のcallback関数を渡すことになる。

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投稿日：2019-11-15T00:41:35+09:00

Flex Delegateを有効にしたTensorflow Liteの共有ライブラリのビルドとTensorflow Liteで非対応のオペレーションを含むモデルのtflite変換・8bit量子化 (Tensorflow v2.0.0)

TensorFlow-ENet　

１．Introduction

Tensorflow Select (Flex Delegate) は、 MaxPoolWithArgmax → FlexMaxPoolWithArgmax や ScatterNd → FlexScatterNd のように、Tensorflow Lite では非対応のオペレーションの先頭に Flex というサフィックスを付与し、Tensorflowへオフロードして実行できるようにする、そうです。が、私には C や C++ でプログラムを実装するスキルが無いため、Flex Delegate を有効にした Tensorflow Lite の共有ライブラリ(.so) のビルドと Tensorflow Lite 非対応モデルの tflite変換＋ 8bit量子化だけ実施して推論のテストは実施せずに自己満足しています。生成したTensorflow Liteの共有ライブラリ libtensorflowlite.so は x86_64 のアーキテクチャ上でビルドしました。 ビルド済みの libtensorflow.so (x86_64) のダウンロードリンク (Google Drive)

Python版の Flex Delegate 対応を待ち望んでいるのですが、公式リポジトリをウォッチしていると、しばらくは期待できそうにありません。現状では Java版のみ正常に動作し、 C/C++版は「まともに動かない」という 【説１】 Shared library libtensorflowlite.so cannot be found after building from source と 【説２】 How to invoke the Flex delegate for tflite interpreters? があります。

ちなみに、Pythonで無理やり実行したところ、下図のようにエラーになりました。

Tensorflow v2.0.0 + Flex + ENet + Python を実行してみました。エラーの内容が変わりましたね。
F tensorflow/core/framework/op.cc:55] Non-OK-status: RegisterAlreadyLocked(op_data_factory) status: Already exists: Op with name Copy pic.twitter.com/x7UJ1C7vDm
— Super PINTO (@PINTO03091) November 9, 2019

２．Environment

Ubuntu 18.04 x86_64 (glibc2.27)
Tensorflow v2.0.0
Tensorflow Lite
Bazel 0.26.1
C or C++ ？？？

３．Procedure

３−１．Generate ENet 8bit quantization model (.tflite)

Tensorflow Lite非対応のオペレーションを含む ENet モデルを Flex Delegate で強制的に tflite形式へ変換すると同時に、8bit量子化も実施します。素の Tensorflow のオペレーションが取り込まれるためか、モデルのサイズは大きくなってしまいます。

Tensorflow_Lite非対応モデル「ENet」のtflite変換と8bit量子化

$ cd ~

### PINTOが独自生成した ENetモデル(.pb) を含むリポジトリのClone
$ git clone https://github.com/PINTO0309/TensorFlow-ENet.git

### Tensorflow v2.0.0 のClone
$ git clone -b v2.0.0 https://github.com/tensorflow/tensorflow.git

### .pbの.tflite変換と8bit量子化
$ cd tensorflow/tensorflow/lite/python
$ sudo bazel run --define=tflite_convert_with_select_tf_ops=true \
tflite_convert -- \
--output_file=${HOME}/TensorFlow-ENet/checkpoint/semanticsegmentation_enet.tflite \
--graph_def_file=${HOME}/TensorFlow-ENet/checkpoint/semanticsegmentation_enet.pb \
--output_format=TFLITE \
--inference_type QUANTIZED_UINT8 \
--input_arrays=input \
--output_arrays=ENet/logits_to_softmax \
--target_ops=TFLITE_BUILTINS,SELECT_TF_OPS \
--post_training_quantize

３−２．Structure of .pb file before conversion (ENet)

semanticsegmentation_enet.pb
PNG Format　SVG Format

３−３．Structure of .tflite file after conversion (ENet)

semanticsegmentation_enet.tflite
PNG Format　SVG Format

３−４．Build a TensorflowLite shared library (.so) with FlexDelegate enabled

ビルド用ディレクトリへ移動

$ cd ~/tensorflow/tensorflow/lite
$ nano BUILD

FlexDelegateを有効にするためBUILDファイルを修正

tflite_cc_shared_object(
    name = "libtensorflowlite.so",
    linkopts = select({
        "//tensorflow:macos": [
            "-Wl,-exported_symbols_list,$(location //tensorflow/lite:tflite_exported_symbols.lds)",
            "-Wl,-install_name,@rpath/libtensorflowlite.so",
        ],
        "//tensorflow:windows": [],
        "//conditions:default": [
            "-z defs",
            "-Wl,--version-script,$(location //tensorflow/lite:tflite_version_script.lds)",
        ],
    }),
    deps = [
        ":framework",
        ":tflite_exported_symbols.lds",
        ":tflite_version_script.lds",
        "//tensorflow/lite/kernels:builtin_ops",
        "//tensorflow/lite/delegates/flex:delegate",
    ],
)

Ctrl + O
Ctrl + X

FlexDelegateを有効にしたTensorflowLiteの共有ライブラリ(.so)のビルド

$ sudo bazel build \
--config=monolithic \
--config=noaws \
--config=nohdfs \
--config=noignite \
--config=nokafka \
--config=nonccl \
--config=v2 \
--define=tflite_convert_with_select_tf_ops=true \
--define=with_select_tf_ops=true \
//tensorflow/lite:libtensorflowlite.so

Tensorflow v2.0.0 本体のカーネルをすべて取り込んでいるようで、 95.5 MB もの巨大な共有ライブラリが生成されました。全然ライトじゃないです。。。

libtensorflowlite.so のシンボル参照

シンボル参照

$ objdump -T libtensorflowlite.so | grep text 0000000000744810 g DF .text 000000000000002b VERS_1.0 _ZN6tflite8IsFlexOpEPKc 000000000060e540 g DF .text 0000000000000047 VERS_1.0 _ZN6tflite16ResourceVariableD2Ev 00000000006199b0 g DF .text 00000000000027cc VERS_1.0 _ZN6tflite11ParseOpDataEPKNS_8OperatorENS_15BuiltinOperatorEPNS_13ErrorReporterEPNS_20BuiltinDataAllocatorEPPv 00000000005f1c20 g DF .text 0000000000000079 VERS_1.0 _ZN6tflite8Subgraph11CleanupNodeEi 00000000005f0f90 g DF .text 0000000000000032 VERS_1.0 _ZN6tflite12tensor_utils32PortableVectorVectorCwiseProductEPKfS2_iPf 0000000000559730 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops6custom11Register_IFEv 00000000004c1ba0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin13Register_TANHEv 00000000004e7720 g DF .text 0000000000000262 VERS_1.0 _ZN6tflite3ops7builtin4ceil4EvalEP13TfLiteContextP10TfLiteNode 0000000000526940 w DF .text 0000000000000006 VERS_1.0 _ZN6tflite13optimized_ops23DepthwiseConvWorkerTaskIhiED1Ev 000000000055c570 g DF .text 000000000000000a VERS_1.0 _ZN6tflite3ops7builtin37Register_LOCAL_RESPONSE_NORMALIZATIONEv 00000000005f2e90 g DF .text 000000000000013b VERS_1.0 _ZN6tflite8Subgraph20PrepareOpsAndTensorsEv 000000000053af70 w DF .text 0000000000000a71 VERS_1.0 _ZN6tflite3ops7builtin3div7EvalDivILNS2_10KernelTypeE1EEEvP13TfLiteContextP10TfLiteNodeP15TfLiteDivParamsPKNS2_6OpDataEPK12TfLiteTensorSG_PSE_ 00000000004c1f30 w DF .text 000000000000031f VERS_1.0 _ZN6tflite3ops7builtin11activations16SoftmaxQuantizedIhEE12TfLiteStatusP13TfLiteContextPK12TfLiteTensorPS7_PNS2_13SoftmaxOpDataE 0000000000538d20 w DF .text 00000000000005b3 VERS_1.0 _ZN6tflite13reference_ops18BroadcastDiv4DSlowIiEEvRKNS_16ArithmeticParamsERKNS_12RuntimeShapeEPKT_S7_SA_S7_PS8_ 0000000000600630 g DF .text 00000000000000db VERS_1.0 _ZN6tflite12ArenaPlannerD2Ev 0000000000744200 g DF .text 0000000000000086 VERS_1.0 _ZN6tflite16logging_internal13MinimalLogger3LogENS_11LogSeverityEPKcz 00000000004d6c50 g DF .text 000000000000022c VERS_1.0 _ZN6tflite3ops7builtin11arg_min_max7PrepareEP13TfLiteContextP10TfLiteNode 000000000059ec50 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin14Register_SHAPEEv 0000000000744080 g DF .text 000000000000001a VERS_1.0 _ZN6tflite4flex21GetTensorFlowLiteTypeE11TF_DataType 00000000006063c0 w DF .text 00000000000000b5 VERS_1.0 _ZN6tflite8delegate5nnapi14NNAPIOpBuilder16AddScalarOperandIfEE12TfLiteStatusT_i 00000000005dcf60 g DF .text 00000000000000c1 VERS_1.0 _ZN6tflite13eigen_support21DecrementUsageCounterEP13TfLiteContext 00000000004cc5f0 w DF .text 0000000000001389 VERS_1.0 _ZN6tflite3ops7builtin3add7EvalAddILNS2_10KernelTypeE1EEEvP13TfLiteContextP10TfLiteNodeP15TfLiteAddParamsPKNS2_6OpDataEPK12TfLiteTensorSG_PSE_ 00000000005ad2f0 w DF .text 00000000000003ed VERS_1.0 _ZN6tflite3ops7builtin15sparse_to_dense17SparseToDenseImplIaiEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 000000000058ea70 g DF .text 000000000000009e VERS_1.0 _ZN6tflite3ops7builtin7reshape4EvalEP13TfLiteContextP10TfLiteNode 00000000004c1bb0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin21Register_LOGISTIC_REFEv 0000000000577b50 w DF .text 0000000000000427 VERS_1.0 _ZN6tflite13reference_ops7PadImplIffEEvRKNS_9PadParamsERKNS_12RuntimeShapeEPKT_PKT0_S7_PS8_ 000000000061cae0 g DF .text 0000000000000069 VERS_1.0 _ZN6tflite18QuantizeMultiplierEdPiS0_ 0000000000543d20 g DF .text 000000000000000a VERS_1.0 _ZN6tflite3ops7builtin24Register_FULLY_CONNECTEDEv 0000000000551450 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin15Register_GATHEREv 00000000005fe1d0 g DF .text 000000000000045b VERS_1.0 _ZN6tflite18InterpreterBuilder12ParseTensorsEPKN11flatbuffers6VectorINS1_6OffsetINS_6BufferEEEEEPKNS2_INS3_INS_6TensorEEEEEPNS_8SubgraphE 00000000005ef230 g DF .text 00000000000002d4 VERS_1.0 _ZN6tflite17CpuBackendContextD1Ev 00000000004d5300 w DF .text 0000000000000092 VERS_1.0 _ZNSt6vectorIN6tflite12RuntimeShapeESaIS1_EED2Ev 0000000000580850 w DF .text 00000000000004e1 VERS_1.0 _ZN6tflite3ops7builtin7pooling20MaxEvalQuantizedInt8ILNS2_10KernelTypeE0EEEvP13TfLiteContextP10TfLiteNodeP16TfLitePoolParamsPNS2_6OpDataEPK12TfLiteTensorPSD_ 0000000000535220 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin3div4FreeEP13TfLiteContextPv 0000000000587b60 w DF .text 000000000000000e VERS_1.0 _ZZN6tflite3ops7builtin6reduce8EvalTypeIaEE12TfLiteStatusP13TfLiteContextP10TfLiteNodePNS2_9OpContextENS2_10ReduceTypeEENUlaaE2_4_FUNEaa 00000000004c7a90 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin3add4FreeEP13TfLiteContextPv 00000000005fd990 g DF .text 000000000000054a VERS_1.0 _ZN6tflite18InterpreterBuilder10ParseNodesEPKN11flatbuffers6VectorINS1_6OffsetINS_8OperatorEEEEEPNS_8SubgraphE 0000000000556710 w DF .text 0000000000000522 VERS_1.0 _ZN6tflite3ops7builtin9gather_nd8GatherNdIliEE12TfLiteStatusPK12TfLiteTensorS7_PS5_ 000000000060a880 g DF .text 0000000000000970 VERS_1.0 _ZN6tflite8delegate5nnapi19NNAPIDelegateKernel16AddOpsAndTensorsEP13TfLiteContext 00000000004f7790 w DF .text 0000000000000093 VERS_1.0 _ZN6tflite15VectorOfTensorsIfED1Ev 00000000007444d0 g DF .text 000000000000000e VERS_1.0 _ZN6tflite9GetStringEPK12TfLiteTensori 00000000004ff5e0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin38Register_CONVOLUTION_MULTITHREADED_OPTEv 00000000005f44f0 g DF .text 000000000000010e VERS_1.0 _ZN6tflite8Subgraph10AddTensorsEiPi 00000000005639f0 g DF .text 000000000000000a VERS_1.0 _ZN6tflite3ops7builtin16Register_MAXIMUMEv 00000000005ff9b0 w DF .text 0000000000000112 VERS_1.0 _ZNSt10_HashtableISt4pairINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEiES0_IKS7_18TfLiteRegistrationESaISA_ENSt8__detail10_Select1stESt8equal_toIS7_EN6tflite18op_resolver_hasher17OperatorKeyHasherIS7_EENSC_18_Mod_range_hashingENSC_20_Default_ranged_hashENSC_20_Prime_rehash_policyENSC_17_Hashtable_traitsILb1ELb0ELb1EEEE9_M_rehashEmRKm 00000000005d2ce0 g DF .text 000000000000071a VERS_1.0 _ZN6tflite3ops7builtin28unidirectional_sequence_lstm7PrepareEP13TfLiteContextP10TfLiteNode 00000000005c75f0 g DF .text 000000000000023e VERS_1.0 _ZN6tflite3ops7builtin4svdf4EvalEP13TfLiteContextP10TfLiteNode 000000000053d2b0 g DF .text 0000000000000126 VERS_1.0 _ZN6tflite3ops7builtin16embedding_lookup10EvalHybridEP13TfLiteContextP10TfLiteNodePK12TfLiteTensorS9_PS7_ 0000000000505270 w DF .text 000000000000052a VERS_1.0 _ZN6tflite3ops7builtin4conv10EvalHybridILNS2_10KernelTypeE2EEEvP13TfLiteContextP10TfLiteNodeP16TfLiteConvParamsPNS2_6OpDataEP12TfLiteTensorSE_SE_SE_SE_SE_ 00000000005e4410 g DF .text 0000000000000021 VERS_1.0 _ZN6tflite8internal7MfccDctC2Ev 000000000055f8e0 w DF .text 0000000000000034 VERS_1.0 _ZNSt6vectorIPKN6tflite12RuntimeShapeESaIS3_EE12emplace_backIJS3_EEEvDpOT_ 0000000000601c40 w DF .text 000000000000000b VERS_1.0 _ZN6tflite8delegate5nnapi14BasicMappingFnILi48EEEiRKNS1_18NNAPIOpMappingArgsE 00000000005ef100 g DF .text 000000000000000a VERS_1.0 _ZN6tflite12tensor_utils34VectorVectorCwiseProductAccumulateEPKfS2_iPf 00000000005f2b10 g DF .text 000000000000000e VERS_1.0 _ZN6tflite8Subgraph12ResizeTensorEP13TfLiteContextP12TfLiteTensorP14TfLiteIntArray 0000000000576c50 g DF .text 00000000000002a7 VERS_1.0 _ZN6tflite3ops7builtin3pad7PrepareEP13TfLiteContextP10TfLiteNode 000000000055d6e0 w DF .text 000000000000005f VERS_1.0 _ZN6tflite12RuntimeShapeC1ERKS0_ 0000000000528710 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin34Register_DEPTHWISE_CONVOLUTION_REFEv 00000000004bc680 g DF .text 0000000000000049 VERS_1.0 _ZN6tflite3ops7builtin11activations14LogSoftmaxInitEP13TfLiteContextPKcm 0000000000542720 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin15fully_connected4FreeEP13TfLiteContextPv 00000000004c7360 w DF .text 00000000000006f3 VERS_1.0 _ZN6tflite3ops7builtin11activations8TanhEvalILNS2_10KernelTypeE1EEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 0000000000616550 g DF .text 0000000000000044 VERS_1.0 _ZN6tflite4flex12DelegateDataC2Ev 00000000005d0e20 w DF .text 000000000000032c VERS_1.0 _ZN6tflite3ops7builtin14transpose_conv4EvalILNS2_10KernelTypeE0EEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 000000000053dd10 w DF .text 00000000000000f4 VERS_1.0 _ZN6tflite3ops7builtin3exp4EvalILNS2_10KernelTypeE0EEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 0000000000588db0 w DF .text 000000000000000d VERS_1.0 _ZZN6tflite3ops7builtin6reduce8EvalTypeIfEE12TfLiteStatusP13TfLiteContextP10TfLiteNodePNS2_9OpContextENS2_10ReduceTypeEENKUlffE2_clEff 00000000005ef160 g DF .text 000000000000000a VERS_1.0 _ZN6tflite12tensor_utils23VectorBatchVectorAssignEPKfiiPf 00000000004e4f70 g DF .text 00000000000008d7 VERS_1.0 _ZN6tflite3ops7builtin26bidirectional_sequence_rnn7PrepareEP13TfLiteContextP10TfLiteNode 00000000004e7640 g DF .text 00000000000000d9 VERS_1.0 _ZN6tflite3ops7builtin4ceil7PrepareEP13TfLiteContextP10TfLiteNode 000000000055d070 g DF .text 00000000000001ce VERS_1.0 _ZN6tflite3ops7builtin14lsh_projection6ResizeEP13TfLiteContextP10TfLiteNode 000000000053dc30 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin32Register_EMBEDDING_LOOKUP_SPARSEEv 00000000005b9d60 g DF .text 0000000000000013 VERS_1.0 _ZN6tflite3ops7builtin3sub4InitEP13TfLiteContextPKcm 0000000000571020 w DF .text 00000000000005c4 VERS_1.0 _ZN6tflite13reference_ops18BroadcastMul4DSlowIfEEvRKNS_16ArithmeticParamsERKNS_12RuntimeShapeEPKT_S7_SA_S7_PS8_ 0000000000588de0 w DF .text 000000000000000d VERS_1.0 _ZZN6tflite3ops7builtin6reduce8EvalTypeIiEE12TfLiteStatusP13TfLiteContextP10TfLiteNodePNS2_9OpContextENS2_10ReduceTypeEENKUliiE1_clEii 00000000004f7830 w DF .text 0000000000000093 VERS_1.0 _ZN6tflite15VectorOfTensorsIiED1Ev 0000000000601c30 w DF .text 000000000000000b VERS_1.0 _ZN6tflite8delegate5nnapi14BasicMappingFnILi54EEEiRKNS1_18NNAPIOpMappingArgsE 00000000005f11a0 g DF .text 0000000000000056 VERS_1.0 _ZN6tflite12tensor_utils28PortableVectorBatchVectorAddEPKfiiPf 00000000005ba390 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin21Register_SUB_NEON_OPTEv 0000000000569820 g DF .text 0000000000000563 VERS_1.0 _ZN6tflite3ops7builtin10mirror_pad4EvalEP13TfLiteContextP10TfLiteNode 00000000005f4060 g DF .text 0000000000000172 VERS_1.0 _ZN6tflite8Subgraph16UndoAllDelegatesEv 000000000057fdf0 w DF .text 0000000000000089 VERS_1.0 _ZN6tflite3ops7builtin7pooling11AverageEvalILNS2_10KernelTypeE0EEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 0000000000563eb0 w DF .text 0000000000000272 VERS_1.0 _ZN6tflite3ops7builtin15maximum_minimum15TFLiteOperationIfNS2_9MaximumOpEEEvP13TfLiteContextP10TfLiteNodeRKNS2_9OpContextE 000000000052dcf0 w DF .text 00000000000003dd VERS_1.0 _ZN6tflite3ops7builtin14depthwise_conv13EvalQuantizedILNS2_10KernelTypeE1EEEvP13TfLiteContextP10TfLiteNodeP25TfLiteDepthwiseConvParamsPNS2_6OpDataEPK12TfLiteTensorSF_SF_PSD_ 00000000005b4630 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin27Register_SQUARED_DIFFERENCEEv 00000000005a0c20 w DF .text 00000000000002ef VERS_1.0 _ZN6tflite13optimized_ops5SliceIiEEvRKNS_11SliceParamsERKNS_12RuntimeShapeES7_PNS_22SequentialTensorWriterIT_EE 000000000061d170 g DF .text 000000000000005a VERS_1.0 _ZN6tflite11CheckedLog2EfPi 0000000000574e70 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin13Register_PACKEv 0000000000588e50 w DF .text 000000000000000b VERS_1.0 _ZZN6tflite3ops7builtin6reduce8EvalTypeIhEE12TfLiteStatusP13TfLiteContextP10TfLiteNodePNS2_9OpContextENS2_10ReduceTypeEENKUlhhE0_clEhh 00000000005e8e90 g DF .text 0000000000000701 VERS_1.0 _ZN6tflite12kernel_utils12RnnBatchStepEPKfPKafS2_S4_fS4_fS2_iiiii21TfLiteFusedActivationPaS6_S6_PfS7_S7_ 00000000004d52f0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin14Register_ADD_NEv 000000000060ea40 w DF .text 00000000000000f7 VERS_1.0 _ZNSt10_HashtableIPK13TfLiteContextSt4pairIKS2_N6tflite4flex9BufferMapEESaIS8_ENSt8__detail10_Select1stESt8equal_toIS2_ESt4hashIS2_ENSA_18_Mod_range_hashingENSA_20_Default_ranged_hashENSA_20_Prime_rehash_policyENSA_17_Hashtable_traitsILb0ELb0ELb1EEEE21_M_insert_unique_nodeEmmPNSA_10_Hash_nodeIS8_Lb0EEE 0000000000504eb0 w DF .text 00000000000003bd VERS_1.0 _ZN6tflite3ops7builtin4conv9EvalFloatILNS2_10KernelTypeE1EEEvP13TfLiteContextP10TfLiteNodeP16TfLiteConvParamsPNS2_6OpDataEP12TfLiteTensorSE_SE_SE_SE_SE_ 0000000000500030 w DF .text 0000000000000418 VERS_1.0 _ZN6tflite13optimized_ops10HybridConvERKNS_10ConvParamsEPfRKNS_12RuntimeShapeEPKaS7_S9_S7_PKfS7_S4_S7_Pa 00000000005f3be0 g DF .text 0000000000000465 VERS_1.0 _ZN6tflite8Subgraph37ReplaceNodeSubsetsWithDelegateKernelsE18TfLiteRegistrationPK14TfLiteIntArrayP14TfLiteDelegate 00000000005f0c10 g DF .text 00000000000000ed VERS_1.0 _ZN6tflite12tensor_utils43PortableMatrixBatchVectorMultiplyAccumulateEPKaiiS2_PKfiPfi 00000000005b9e10 g DF .text 00000000000001eb VERS_1.0 _ZN6tflite3ops7builtin3sub16Prepare8BitSubOpEP13TfLiteContextPK12TfLiteTensorS7_PS5_P15TfLiteSubParamsPNS2_6OpDataEi 0000000000526940 w DF .text 0000000000000006 VERS_1.0 _ZN6tflite13optimized_ops23DepthwiseConvWorkerTaskIhiED2Ev 00000000004e2840 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin30Register_BATCH_TO_SPACE_ND_REFEv 00000000004ddf40 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops6custom26Register_AUDIO_SPECTROGRAMEv 00000000005ff330 w DF .text 0000000000000068 VERS_1.0 _ZNKSt10_HashtableISt4pairIN6tflite15BuiltinOperatorEiES0_IKS3_18TfLiteRegistrationESaIS6_ENSt8__detail10_Select1stESt8equal_toIS3_ENS1_18op_resolver_hasher17OperatorKeyHasherIS3_EENS8_18_Mod_range_hashingENS8_20_Default_ranged_hashENS8_20_Prime_rehash_policyENS8_17_Hashtable_traitsILb1ELb0ELb1EEEE19_M_find_before_nodeEmRS4_m 00000000005e4b30 g DF .text 0000000000000051 VERS_1.0 _ZN6tflite8internal17MfccMelFilterbankC1Ev 0000000000533770 g DF .text 00000000000003c0 VERS_1.0 _ZN6tflite3ops6custom21detection_postprocess35SelectDetectionsAboveScoreThresholdERKSt6vectorIfSaIfEEfPS5_PS3_IiSaIiEE 0000000000563790 g DF .text 0000000000000045 VERS_1.0 _ZN6tflite3ops7builtin15matrix_set_diag4EvalEP13TfLiteContextP10TfLiteNode 000000000053cfc0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin12Register_SINEv 00000000005f2a10 g DF .text 00000000000000f8 VERS_1.0 _ZN6tflite8Subgraph16ResizeTensorImplEP12TfLiteTensorP14TfLiteIntArray 00000000007440a0 g DF .text 000000000000014d VERS_1.0 _ZN6tflite4flex16CopyShapeAndTypeEP13TfLiteContextRKN10tensorflow6TensorEP12TfLiteTensor 0000000000532f80 g DF .text 000000000000005f VERS_1.0 _ZN6tflite3ops6custom21detection_postprocess14SetTensorSizesEP13TfLiteContextP12TfLiteTensorSt16initializer_listIiE 00000000004f7990 w DF .text 0000000000000093 VERS_1.0 _ZN6tflite15VectorOfTensorsIlED1Ev 00000000005d3420 g DF .text 000000000000002f VERS_1.0 _ZN6tflite3ops7builtin27unidirectional_sequence_rnn4InitEP13TfLiteContextPKcm 0000000000548740 w DF .text 00000000000001a6 VERS_1.0 _ZN6tflite13optimized_ops14FullyConnectedERKNS_20FullyConnectedParamsERKNS_12RuntimeShapeEPKhS6_S8_S6_PKiS6_PhPNS_17CpuBackendContextE 00000000004d1f40 w DF .text 000000000000316d VERS_1.0 _ZN6tflite3ops7builtin3add16EvalAddQuantizedILNS2_10KernelTypeE2EEE12TfLiteStatusP13TfLiteContextP10TfLiteNodeP15TfLiteAddParamsPKNS2_6OpDataEPK12TfLiteTensorSH_PSF_ 00000000005979e0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin19Register_REVERSE_V2Ev 0000000000588e40 w DF .text 0000000000000009 VERS_1.0 _ZZN6tflite3ops7builtin6reduce8EvalTypeIhEE12TfLiteStatusP13TfLiteContextP10TfLiteNodePNS2_9OpContextENS2_10ReduceTypeEENKUlhhE_clEhh 000000000055d050 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin19Register_LOGICAL_OREv 00000000004c1b80 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin14Register_RELU6Ev 00000000005ef230 g DF .text 00000000000002d4 VERS_1.0 _ZN6tflite17CpuBackendContextD2Ev 00000000005ef110 g DF .text 000000000000000a VERS_1.0 _ZN6tflite12tensor_utils29VectorBatchVectorCwiseProductEPKfiS2_iPf 00000000005643b0 w DF .text 000000000000048f VERS_1.0 _ZN6tflite13reference_ops29MaximumMinimumBroadcast4DSlowIhPFhhhEEEvRKNS_12RuntimeShapeEPKT_S6_S9_S6_PS7_T0_ 0000000000601e30 g DF .text 0000000000001dc6 VERS_1.0 _ZN6tflite8delegate5nnapi19NNAPIDelegateKernel3MapEPK13TfLiteContextiiiPK10TfLiteNodeb 00000000005fd8b0 g DF .text 0000000000000082 VERS_1.0 _ZN6tflite18InterpreterBuilderC1EPKNS_5ModelERKNS_10OpResolverEPNS_13ErrorReporterE 0000000000569dc0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin3mul4FreeEP13TfLiteContextPv 00000000004e7ca0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin18Register_NOT_EQUALEv 000000000059ea90 g DF .text 00000000000000a5 VERS_1.0 _ZN6tflite3ops7builtin5shape4EvalEP13TfLiteContextP10TfLiteNode 000000000058eb20 g DF .text 00000000000000d1 VERS_1.0 _ZN6tflite3ops7builtin15resize_bilinear18ResizeOutputTensorEP13TfLiteContextPK12TfLiteTensorS7_PS5_ 00000000005872a0 g DF .text 000000000000000a VERS_1.0 _ZN6tflite3ops7builtin17Register_QUANTIZEEv 0000000000619720 g DF .text 0000000000000087 VERS_1.0 _ZN6tflite13ErrorReporter6ReportEPKcz 0000000000587a60 w DF .text 000000000000000d VERS_1.0 _ZZN6tflite3ops7builtin6reduce8EvalTypeIfEE12TfLiteStatusP13TfLiteContextP10TfLiteNodePNS2_9OpContextENS2_10ReduceTypeEENUlffE2_4_FUNEff 00000000005e8970 w DF .text 00000000000001bf VERS_1.0 _ZN6tflite8internal11Spectrogram34ComputeSquaredMagnitudeSpectrogramIddEEbRKSt6vectorIT_SaIS4_EEPS3_IS3_IT0_SaIS9_EESaISB_EE 00000000005b1e40 w DF .text 0000000000000203 VERS_1.0 _ZN6tflite3ops7builtin7split_v19GetSizeSplitsVectorIiEEvPK12TfLiteTensorPSt6vectorIlSaIlEE 00000000004f7790 w DF .text 0000000000000093 VERS_1.0 _ZN6tflite15VectorOfTensorsIfED2Ev 00000000006018e0 g DF .text 000000000000001f VERS_1.0 _ZN6tflite25ExternalCpuBackendContextC1Ev 00000000005cfa70 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin34Register_TRANSPOSECONV_GENERIC_OPTEv 0000000000576f50 g DF .text 000000000000000a VERS_1.0 _ZN6tflite3ops7builtin14Register_PADV2Ev 0000000000582bb0 w DF .text 00000000000006df VERS_1.0 _ZN6tflite3ops7builtin7pooling21MaxEvalQuantizedUInt8ILNS2_10KernelTypeE1EEEvP13TfLiteContextP10TfLiteNodeP16TfLitePoolParamsPNS2_6OpDataEPK12TfLiteTensorPSD_ 000000000057dcd0 w DF .text 0000000000000739 VERS_1.0 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投稿日：2019-11-15T00:41:35+09:00

Flex Delegateを有効にしたTensorflow Liteの共有ライブラリのビルドとTensorflow Liteで非対応のオペレーションを含むモデル(.pb)のtflite変換・8bit量子化 (Tensorflow v2.0.0)

TensorFlow-ENet　

１．Introduction

Tensorflow Select (Flex Delegate) は、 MaxPoolWithArgmax → FlexMaxPoolWithArgmax や ScatterNd → FlexScatterNd のように、Tensorflow Lite では非対応のオペレーションの先頭に Flex というサフィックスを付与し、Tensorflowへオフロードして実行できるようにする、そうです。が、私には C や C++ でプログラムを実装するスキルが無いため、Flex Delegate を有効にした Tensorflow Lite の共有ライブラリ(.so) のビルドと Tensorflow Lite 非対応モデルの tflite変換＋ 8bit量子化だけ実施して推論のテストは実施せずに自己満足しています。生成したTensorflow Liteの共有ライブラリ libtensorflowlite.so は x86_64 のアーキテクチャ上でビルドしました。 ビルド済みの libtensorflowlite.so (x86_64) のダウンロードリンク (Google Drive)

Python版の Flex Delegate 対応を待ち望んでいるのですが、公式リポジトリをウォッチしている限りではしばらく期待できそうにありません。現状では Java版のみ正常に動作し、 C/C++版は「まともに動かない」という 【説１】 Shared library libtensorflowlite.so cannot be found after building from source と 【説２】 How to invoke the Flex delegate for tflite interpreters? があります。

ちなみに、Pythonで無理やり実行したところ、下図のようにエラーになりました。

Tensorflow v2.0.0 + Flex + ENet + Python を実行してみました。エラーの内容が変わりましたね。
F tensorflow/core/framework/op.cc:55] Non-OK-status: RegisterAlreadyLocked(op_data_factory) status: Already exists: Op with name Copy pic.twitter.com/x7UJ1C7vDm
— Super PINTO (@PINTO03091) November 9, 2019

２．Environment

Ubuntu 18.04 x86_64 (glibc2.27)
Tensorflow v2.0.0
Tensorflow Lite
Bazel 0.26.1
C or C++ ？？？

３．Procedure

３−１．Generate ENet 8bit quantization model (.tflite)

Tensorflow Lite非対応のオペレーションを含むセマンティック・セグメンテーションのモデル ENet モデルを Flex Delegate で強制的に tflite形式へ変換すると同時に、8bit量子化も実施します。素の Tensorflow のオペレーションが取り込まれるためか、モデルのサイズは大きくなってしまいます。

Tensorflow_Lite非対応モデル「ENet」のtflite変換と8bit量子化

$ cd ~

### PINTOが独自生成した ENetモデル(.pb) を含むリポジトリのClone
$ git clone https://github.com/PINTO0309/TensorFlow-ENet.git

### Tensorflow v2.0.0 のClone
$ git clone -b v2.0.0 https://github.com/tensorflow/tensorflow.git

### .pbの.tflite変換と8bit量子化
$ cd tensorflow/tensorflow/lite/python
$ sudo bazel run --define=tflite_convert_with_select_tf_ops=true \
tflite_convert -- \
--output_file=${HOME}/TensorFlow-ENet/checkpoint/semanticsegmentation_enet.tflite \
--graph_def_file=${HOME}/TensorFlow-ENet/checkpoint/semanticsegmentation_enet.pb \
--output_format=TFLITE \
--inference_type QUANTIZED_UINT8 \
--input_arrays=input \
--output_arrays=ENet/logits_to_softmax \
--target_ops=TFLITE_BUILTINS,SELECT_TF_OPS \
--post_training_quantize

３−２．Structure of .pb file before conversion (ENet)

semanticsegmentation_enet.pb
PNG Format　SVG Format

３−３．Structure of .tflite file after conversion (ENet)

semanticsegmentation_enet.tflite
PNG Format　SVG Format

３−４．Build a TensorflowLite shared library (.so) with FlexDelegate enabled

configure

$ cd ~/tensorflow
$ ./configure 
WARNING: ignoring LD_PRELOAD in environment.
WARNING: --batch mode is deprecated. Please instead explicitly shut down your Bazel server using the command "bazel shutdown".
You have bazel 0.26.1- (@non-git) installed.
Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python3

Found possible Python library paths:
  /opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer
  /usr/local/lib
  /opt/intel/openvino_2019.3.376/python/python3.6
  /opt/intel/openvino_2019.3.376/deployment_tools/open_model_zoo/tools/accuracy_checker
  /usr/local/lib/python3.6/dist-packages
  /usr/lib/python3/dist-packages
  /opt/intel/openvino_2019.3.376/python/python3
Please input the desired Python library path to use.  Default is [/opt/intel/openvino_2019.3.376/deployment_tools/model_optimizer]
/usr/local/lib/python3.6/dist-packages
Do you wish to build TensorFlow with XLA JIT support? [Y/n]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with ROCm support? [y/N]: n
No ROCm support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: n
No CUDA support will be enabled for TensorFlow.

Do you wish to download a fresh release of clang? (Experimental) [y/N]: n
Clang will not be downloaded.

Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native -Wno-sign-compare]: 


Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See .bazelrc for more details.
    --config=mkl            # Build with MKL support.
    --config=monolithic     # Config for mostly static monolithic build.
    --config=gdr            # Build with GDR support.
    --config=verbs          # Build with libverbs support.
    --config=ngraph         # Build with Intel nGraph support.
    --config=numa           # Build with NUMA support.
    --config=dynamic_kernels    # (Experimental) Build kernels into separate shared objects.
    --config=v2             # Build TensorFlow 2.x instead of 1.x.
Preconfigured Bazel build configs to DISABLE default on features:
    --config=noaws          # Disable AWS S3 filesystem support.
    --config=nogcp          # Disable GCP support.
    --config=nohdfs         # Disable HDFS support.
    --config=noignite       # Disable Apache Ignite support.
    --config=nokafka        # Disable Apache Kafka support.
    --config=nonccl         # Disable NVIDIA NCCL support.
Configuration finished

ビルド用ディレクトリへ移動

$ cd ~/tensorflow/tensorflow/lite
$ nano BUILD

FlexDelegateを有効にするためBUILDファイルを修正

tflite_cc_shared_object(
    name = "libtensorflowlite.so",
    linkopts = select({
        "//tensorflow:macos": [
            "-Wl,-exported_symbols_list,$(location //tensorflow/lite:tflite_exported_symbols.lds)",
            "-Wl,-install_name,@rpath/libtensorflowlite.so",
        ],
        "//tensorflow:windows": [],
        "//conditions:default": [
            "-z defs",
            "-Wl,--version-script,$(location //tensorflow/lite:tflite_version_script.lds)",
        ],
    }),
    deps = [
        ":framework",
        ":tflite_exported_symbols.lds",
        ":tflite_version_script.lds",
        "//tensorflow/lite/kernels:builtin_ops",
        "//tensorflow/lite/delegates/flex:delegate",
    ],
)

Ctrl + O
Ctrl + X

MakeFileの修正

$ nano ~/tensorflow/tensorflow/lite/tools/make/Makefile

NNAPIを無効化

BUILD_WITH_NNAPI=false

Ctrl + O
Ctrl + X

FlexDelegateを有効にしたTensorflowLiteの共有ライブラリ(.so)のビルド

$ sudo bazel build \
--config=monolithic \
--config=noaws \
--config=nohdfs \
--config=noignite \
--config=nokafka \
--config=nonccl \
--config=v2 \
--define=tflite_convert_with_select_tf_ops=true \
--define=with_select_tf_ops=true \
//tensorflow/lite:libtensorflowlite.so

Tensorflow v2.0.0 本体のカーネルをすべて取り込んでいるようで、 95.5 MB もの巨大な共有ライブラリが生成されました。全然ライトじゃないです。。。

libtensorflowlite.so のシンボル参照

シンボル参照

$ objdump -T libtensorflowlite.so | grep text 0000000000744810 g DF .text 000000000000002b VERS_1.0 _ZN6tflite8IsFlexOpEPKc 000000000060e540 g DF .text 0000000000000047 VERS_1.0 _ZN6tflite16ResourceVariableD2Ev 00000000006199b0 g DF .text 00000000000027cc VERS_1.0 _ZN6tflite11ParseOpDataEPKNS_8OperatorENS_15BuiltinOperatorEPNS_13ErrorReporterEPNS_20BuiltinDataAllocatorEPPv 00000000005f1c20 g DF .text 0000000000000079 VERS_1.0 _ZN6tflite8Subgraph11CleanupNodeEi 00000000005f0f90 g DF .text 0000000000000032 VERS_1.0 _ZN6tflite12tensor_utils32PortableVectorVectorCwiseProductEPKfS2_iPf 0000000000559730 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops6custom11Register_IFEv 00000000004c1ba0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin13Register_TANHEv 00000000004e7720 g DF .text 0000000000000262 VERS_1.0 _ZN6tflite3ops7builtin4ceil4EvalEP13TfLiteContextP10TfLiteNode 0000000000526940 w DF .text 0000000000000006 VERS_1.0 _ZN6tflite13optimized_ops23DepthwiseConvWorkerTaskIhiED1Ev 000000000055c570 g DF .text 000000000000000a VERS_1.0 _ZN6tflite3ops7builtin37Register_LOCAL_RESPONSE_NORMALIZATIONEv 00000000005f2e90 g DF .text 000000000000013b VERS_1.0 _ZN6tflite8Subgraph20PrepareOpsAndTensorsEv 000000000053af70 w DF .text 0000000000000a71 VERS_1.0 _ZN6tflite3ops7builtin3div7EvalDivILNS2_10KernelTypeE1EEEvP13TfLiteContextP10TfLiteNodeP15TfLiteDivParamsPKNS2_6OpDataEPK12TfLiteTensorSG_PSE_ 00000000004c1f30 w DF .text 000000000000031f VERS_1.0 _ZN6tflite3ops7builtin11activations16SoftmaxQuantizedIhEE12TfLiteStatusP13TfLiteContextPK12TfLiteTensorPS7_PNS2_13SoftmaxOpDataE 0000000000538d20 w DF .text 00000000000005b3 VERS_1.0 _ZN6tflite13reference_ops18BroadcastDiv4DSlowIiEEvRKNS_16ArithmeticParamsERKNS_12RuntimeShapeEPKT_S7_SA_S7_PS8_ 0000000000600630 g DF .text 00000000000000db VERS_1.0 _ZN6tflite12ArenaPlannerD2Ev 0000000000744200 g DF .text 0000000000000086 VERS_1.0 _ZN6tflite16logging_internal13MinimalLogger3LogENS_11LogSeverityEPKcz 00000000004d6c50 g DF .text 000000000000022c VERS_1.0 _ZN6tflite3ops7builtin11arg_min_max7PrepareEP13TfLiteContextP10TfLiteNode 000000000059ec50 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin14Register_SHAPEEv 0000000000744080 g DF .text 000000000000001a VERS_1.0 _ZN6tflite4flex21GetTensorFlowLiteTypeE11TF_DataType 00000000006063c0 w DF .text 00000000000000b5 VERS_1.0 _ZN6tflite8delegate5nnapi14NNAPIOpBuilder16AddScalarOperandIfEE12TfLiteStatusT_i 00000000005dcf60 g DF .text 00000000000000c1 VERS_1.0 _ZN6tflite13eigen_support21DecrementUsageCounterEP13TfLiteContext 00000000004cc5f0 w DF .text 0000000000001389 VERS_1.0 _ZN6tflite3ops7builtin3add7EvalAddILNS2_10KernelTypeE1EEEvP13TfLiteContextP10TfLiteNodeP15TfLiteAddParamsPKNS2_6OpDataEPK12TfLiteTensorSG_PSE_ 00000000005ad2f0 w DF .text 00000000000003ed VERS_1.0 _ZN6tflite3ops7builtin15sparse_to_dense17SparseToDenseImplIaiEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 000000000058ea70 g DF .text 000000000000009e VERS_1.0 _ZN6tflite3ops7builtin7reshape4EvalEP13TfLiteContextP10TfLiteNode 00000000004c1bb0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin21Register_LOGISTIC_REFEv 0000000000577b50 w DF .text 0000000000000427 VERS_1.0 _ZN6tflite13reference_ops7PadImplIffEEvRKNS_9PadParamsERKNS_12RuntimeShapeEPKT_PKT0_S7_PS8_ 000000000061cae0 g DF .text 0000000000000069 VERS_1.0 _ZN6tflite18QuantizeMultiplierEdPiS0_ 0000000000543d20 g DF .text 000000000000000a VERS_1.0 _ZN6tflite3ops7builtin24Register_FULLY_CONNECTEDEv 0000000000551450 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin15Register_GATHEREv 00000000005fe1d0 g DF .text 000000000000045b VERS_1.0 _ZN6tflite18InterpreterBuilder12ParseTensorsEPKN11flatbuffers6VectorINS1_6OffsetINS_6BufferEEEEEPKNS2_INS3_INS_6TensorEEEEEPNS_8SubgraphE 00000000005ef230 g DF .text 00000000000002d4 VERS_1.0 _ZN6tflite17CpuBackendContextD1Ev 00000000004d5300 w DF .text 0000000000000092 VERS_1.0 _ZNSt6vectorIN6tflite12RuntimeShapeESaIS1_EED2Ev 0000000000580850 w DF .text 00000000000004e1 VERS_1.0 _ZN6tflite3ops7builtin7pooling20MaxEvalQuantizedInt8ILNS2_10KernelTypeE0EEEvP13TfLiteContextP10TfLiteNodeP16TfLitePoolParamsPNS2_6OpDataEPK12TfLiteTensorPSD_ 0000000000535220 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin3div4FreeEP13TfLiteContextPv 0000000000587b60 w DF .text 000000000000000e VERS_1.0 _ZZN6tflite3ops7builtin6reduce8EvalTypeIaEE12TfLiteStatusP13TfLiteContextP10TfLiteNodePNS2_9OpContextENS2_10ReduceTypeEENUlaaE2_4_FUNEaa 00000000004c7a90 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin3add4FreeEP13TfLiteContextPv 00000000005fd990 g DF .text 000000000000054a VERS_1.0 _ZN6tflite18InterpreterBuilder10ParseNodesEPKN11flatbuffers6VectorINS1_6OffsetINS_8OperatorEEEEEPNS_8SubgraphE 0000000000556710 w DF .text 0000000000000522 VERS_1.0 _ZN6tflite3ops7builtin9gather_nd8GatherNdIliEE12TfLiteStatusPK12TfLiteTensorS7_PS5_ 000000000060a880 g DF .text 0000000000000970 VERS_1.0 _ZN6tflite8delegate5nnapi19NNAPIDelegateKernel16AddOpsAndTensorsEP13TfLiteContext 00000000004f7790 w DF .text 0000000000000093 VERS_1.0 _ZN6tflite15VectorOfTensorsIfED1Ev 00000000007444d0 g DF .text 000000000000000e VERS_1.0 _ZN6tflite9GetStringEPK12TfLiteTensori 00000000004ff5e0 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin38Register_CONVOLUTION_MULTITHREADED_OPTEv 00000000005f44f0 g DF .text 000000000000010e VERS_1.0 _ZN6tflite8Subgraph10AddTensorsEiPi 00000000005639f0 g DF .text 000000000000000a VERS_1.0 _ZN6tflite3ops7builtin16Register_MAXIMUMEv 00000000005ff9b0 w DF .text 0000000000000112 VERS_1.0 _ZNSt10_HashtableISt4pairINSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEEiES0_IKS7_18TfLiteRegistrationESaISA_ENSt8__detail10_Select1stESt8equal_toIS7_EN6tflite18op_resolver_hasher17OperatorKeyHasherIS7_EENSC_18_Mod_range_hashingENSC_20_Default_ranged_hashENSC_20_Prime_rehash_policyENSC_17_Hashtable_traitsILb1ELb0ELb1EEEE9_M_rehashEmRKm 00000000005d2ce0 g DF .text 000000000000071a VERS_1.0 _ZN6tflite3ops7builtin28unidirectional_sequence_lstm7PrepareEP13TfLiteContextP10TfLiteNode 00000000005c75f0 g DF .text 000000000000023e VERS_1.0 _ZN6tflite3ops7builtin4svdf4EvalEP13TfLiteContextP10TfLiteNode 000000000053d2b0 g DF .text 0000000000000126 VERS_1.0 _ZN6tflite3ops7builtin16embedding_lookup10EvalHybridEP13TfLiteContextP10TfLiteNodePK12TfLiteTensorS9_PS7_ 0000000000505270 w DF .text 000000000000052a VERS_1.0 _ZN6tflite3ops7builtin4conv10EvalHybridILNS2_10KernelTypeE2EEEvP13TfLiteContextP10TfLiteNodeP16TfLiteConvParamsPNS2_6OpDataEP12TfLiteTensorSE_SE_SE_SE_SE_ 00000000005e4410 g DF .text 0000000000000021 VERS_1.0 _ZN6tflite8internal7MfccDctC2Ev 000000000055f8e0 w DF .text 0000000000000034 VERS_1.0 _ZNSt6vectorIPKN6tflite12RuntimeShapeESaIS3_EE12emplace_backIJS3_EEEvDpOT_ 0000000000601c40 w DF .text 000000000000000b VERS_1.0 _ZN6tflite8delegate5nnapi14BasicMappingFnILi48EEEiRKNS1_18NNAPIOpMappingArgsE 00000000005ef100 g DF .text 000000000000000a VERS_1.0 _ZN6tflite12tensor_utils34VectorVectorCwiseProductAccumulateEPKfS2_iPf 00000000005f2b10 g DF .text 000000000000000e VERS_1.0 _ZN6tflite8Subgraph12ResizeTensorEP13TfLiteContextP12TfLiteTensorP14TfLiteIntArray 0000000000576c50 g DF .text 00000000000002a7 VERS_1.0 _ZN6tflite3ops7builtin3pad7PrepareEP13TfLiteContextP10TfLiteNode 000000000055d6e0 w DF .text 000000000000005f VERS_1.0 _ZN6tflite12RuntimeShapeC1ERKS0_ 0000000000528710 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin34Register_DEPTHWISE_CONVOLUTION_REFEv 00000000004bc680 g DF .text 0000000000000049 VERS_1.0 _ZN6tflite3ops7builtin11activations14LogSoftmaxInitEP13TfLiteContextPKcm 0000000000542720 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin15fully_connected4FreeEP13TfLiteContextPv 00000000004c7360 w DF .text 00000000000006f3 VERS_1.0 _ZN6tflite3ops7builtin11activations8TanhEvalILNS2_10KernelTypeE1EEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 0000000000616550 g DF .text 0000000000000044 VERS_1.0 _ZN6tflite4flex12DelegateDataC2Ev 00000000005d0e20 w DF .text 000000000000032c VERS_1.0 _ZN6tflite3ops7builtin14transpose_conv4EvalILNS2_10KernelTypeE0EEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 000000000053dd10 w DF .text 00000000000000f4 VERS_1.0 _ZN6tflite3ops7builtin3exp4EvalILNS2_10KernelTypeE0EEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 0000000000588db0 w DF .text 000000000000000d VERS_1.0 _ZZN6tflite3ops7builtin6reduce8EvalTypeIfEE12TfLiteStatusP13TfLiteContextP10TfLiteNodePNS2_9OpContextENS2_10ReduceTypeEENKUlffE2_clEff 00000000005ef160 g DF .text 000000000000000a VERS_1.0 _ZN6tflite12tensor_utils23VectorBatchVectorAssignEPKfiiPf 00000000004e4f70 g DF .text 00000000000008d7 VERS_1.0 _ZN6tflite3ops7builtin26bidirectional_sequence_rnn7PrepareEP13TfLiteContextP10TfLiteNode 00000000004e7640 g DF .text 00000000000000d9 VERS_1.0 _ZN6tflite3ops7builtin4ceil7PrepareEP13TfLiteContextP10TfLiteNode 000000000055d070 g DF .text 00000000000001ce VERS_1.0 _ZN6tflite3ops7builtin14lsh_projection6ResizeEP13TfLiteContextP10TfLiteNode 000000000053dc30 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin32Register_EMBEDDING_LOOKUP_SPARSEEv 00000000005b9d60 g DF .text 0000000000000013 VERS_1.0 _ZN6tflite3ops7builtin3sub4InitEP13TfLiteContextPKcm 0000000000571020 w DF .text 00000000000005c4 VERS_1.0 _ZN6tflite13reference_ops18BroadcastMul4DSlowIfEEvRKNS_16ArithmeticParamsERKNS_12RuntimeShapeEPKT_S7_SA_S7_PS8_ 0000000000588de0 w DF .text 000000000000000d VERS_1.0 _ZZN6tflite3ops7builtin6reduce8EvalTypeIiEE12TfLiteStatusP13TfLiteContextP10TfLiteNodePNS2_9OpContextENS2_10ReduceTypeEENKUliiE1_clEii 00000000004f7830 w DF .text 0000000000000093 VERS_1.0 _ZN6tflite15VectorOfTensorsIiED1Ev 0000000000601c30 w DF .text 000000000000000b VERS_1.0 _ZN6tflite8delegate5nnapi14BasicMappingFnILi54EEEiRKNS1_18NNAPIOpMappingArgsE 00000000005f11a0 g DF .text 0000000000000056 VERS_1.0 _ZN6tflite12tensor_utils28PortableVectorBatchVectorAddEPKfiiPf 00000000005ba390 g DF .text 000000000000000d VERS_1.0 _ZN6tflite3ops7builtin21Register_SUB_NEON_OPTEv 0000000000569820 g DF .text 0000000000000563 VERS_1.0 _ZN6tflite3ops7builtin10mirror_pad4EvalEP13TfLiteContextP10TfLiteNode 00000000005f4060 g DF .text 0000000000000172 VERS_1.0 _ZN6tflite8Subgraph16UndoAllDelegatesEv 000000000057fdf0 w DF .text 0000000000000089 VERS_1.0 _ZN6tflite3ops7builtin7pooling11AverageEvalILNS2_10KernelTypeE0EEE12TfLiteStatusP13TfLiteContextP10TfLiteNode 0000000000563eb0 w DF .text 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４．Refernce articles

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20191115のTensorFlowに関する記事は6件です。

Winsows10でゼロから機械学習環境を作ってみる

はじめに

PC環境

nVIDIA CUDA ToolkitとcuDNNのインストール

Anacondaのインストール

仮想環境作成

必要そうなパッケージのインストール

OpenAI Gym環境インストール

KerasとTensorFlow環境インストール

完成！

まとめ

TensorFlow2.0エラー

出たエラー

解決策

結果

参考にしたサイト

TypeError: The added layer must be an instance of class Layer. Found: <keras.engine.training.Model object at XXXXXXXX>

エラー内容

原因

解決

TensorFlow World Extended Tokyo 2019 @Google渋谷オフィスへ参加してきて感じたTensorFlow2.0の進化

はじめに

感想

気になったキーワード

わからんワード

以下はメモ。当日のスライドは後日運営の方がUPしてくださるらしいです。

TensorFlow のエコシステムとTensorFlow

TensorFlow2.0の開発体制

TensorFlow Hub

TensorFlow Enterprise

TensorFlow Lite のアップデート

TensorFlow Lite Micro

TensorFlow2.0とTensorBoard.dev

TensorFlow2.0の変更点

TensorBoard

Flex Delegateを有効にしたTensorflow Liteの共有ライブラリのビルドとTensorflow Liteで非対応のオペレーションを含むモデルのtflite変換・8bit量子化 (Tensorflow v2.0.0)

１．Introduction

２．Environment

３．Procedure

３−１．Generate ENet 8bit quantization model (.tflite)

３−２．Structure of .pb file before conversion (ENet)

３−３．Structure of .tflite file after conversion (ENet)

３−４．Build a TensorflowLite shared library (.so) with FlexDelegate enabled

４．Refernce articles

Flex Delegateを有効にしたTensorflow Liteの共有ライブラリのビルドとTensorflow Liteで非対応のオペレーションを含むモデル(.pb)のtflite変換・8bit量子化 (Tensorflow v2.0.0)

１．Introduction

２．Environment

３．Procedure

３−１．Generate ENet 8bit quantization model (.tflite)

３−２．Structure of .pb file before conversion (ENet)

３−３．Structure of .tflite file after conversion (ENet)

３−４．Build a TensorflowLite shared library (.so) with FlexDelegate enabled

４．Refernce articles

日付別

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