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ONNX -> TFLite, TensorFlow, Keras, TFJS, CoreML 変換

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TensorFlow
Keras
tflite
ONNX
tfjs
tech

およそ３光年ぶりの公開記事。

1. 自作ツール onnx2tf

実装している機能が多すぎるので、この記事に表現していない変換オプションはREADMEを参照。TransformerやSTTやTTSやその他もろもろの一発変換に対応したつもり。onnx-tensorflow より推論効率が高いモデルを生成できる。また、onnx-tensorflowよりも対応しているオペレーションの種類が多い。

https://github.com/PINTO0309/onnx2tf

コントリビューター

コード量（code = 行）

cloc .

     419 text files.
     414 unique files.                                          
     174 files ignored.

----------------------------------------
Language    files  blank  comment   code
----------------------------------------
Python        300   4820     6637  37182
JSON           27    109        0   3107
Markdown        5    343        0   2061
YAML            7     42       79    586
Dockerfile      1      6        3     38
----------------------------------------
SUM:          340   5320     6719  42974
----------------------------------------

2. 使用方法

  • onnx==1.13.1
  • onnxruntime==1.13.1
  • onnx-simplifier==0.4.17
  • onnx_graphsurgeon
  • simple_onnx_processing_tools
  • tensorflow==2.13.0rc0

2-1. インストールとサンプルモデルダウンロード

  • docker
docker run --rm -it \
-v `pwd`:/workdir \
-w /workdir \
ghcr.io/pinto0309/onnx2tf:latest
  • pip
pip install onnx2tf -U
  • モデル
wget https://github.com/PINTO0309/onnx2tf/releases/download/0.0.2/resnet18-v1-7.onnx

2-2. 基本の「き」

TFLite を出力。

onnx2tf -i resnet18-v1-7.onnx

2-3. 基本の「ほ」

TFLite変換 + 完全体の saved_model を出力。

onnx2tf -i resnet18-v1-7.onnx -osd

2-4. 基本の「ん」

TFLite変換 + 全OPの精度チェック。

onnx2tf -i resnet18-v1-7.onnx -cotof

2-5. Keras

.h5 はイロイロと問題があるので、現状最新のフォーマット keras_v3 フォーマットで出力。

onnx2tf -i resnet18-v1-7.onnx -okv3

.h5 を生成するとき。

onnx2tf -i resnet18-v1-7.onnx -oh5

2-6. TFLite の入出力名を魔改造

# Custom flatc binary for Ubuntu 20.04+
# https://github.com/PINTO0309/onnx2tf/issues/196
wget https://github.com/PINTO0309/onnx2tf/releases/download/1.7.3/flatc.tar.gz \
  && tar -zxvf flatc.tar.gz \
  && sudo chmod +x flatc \
  && sudo mv flatc /usr/bin/

# Custom flatc binary for Windows
# Set the environment variable paths appropriately on your own.
# https://github.com/PINTO0309/onnx2tf/issues/196
https://github.com/PINTO0309/onnx2tf/releases/download/1.7.3/flatc.exe

onnx2tf -i resnet18-v1-7.onnx -coion

2-7. INT8量子化 (per-channel)

onnx2tf -i resnet18-v1-7.onnx -oiqt -qt per-channel

2-8. INT8量子化 (per-tensor)

onnx2tf -i resnet18-v1-7.onnx -oiqt -qt per-tensor

2-9. TFJS変換

onnx2tf -i resnet18-v1-7.onnx -osd

pip install tensorflowjs

tensorflowjs_converter \
--input_format tf_saved_model \
--output_format tfjs_graph_model \
saved_model \
tfjs_model

2-10. CoreML変換

onnx2tf -i resnet18-v1-7.onnx -osd -k data

pip install coremltools

import coremltools as ct

FOLDER_PATH = 'saved_model'

model = ct.convert(
    model=FOLDER_PATH,
    source='tensorflow',
)
model.save(f'{FOLDER_PATH}/model.mlmodel')

3. 対応レイヤー

OP Status
Abs ✅
Acosh ✅
Acos ✅
Add ✅
And ✅
ArgMax ✅
ArgMin ✅
Asinh ✅
Asin ✅
Atanh ✅
Atan ✅
AveragePool ✅
BatchNormalization ✅
Bernoulli ✅
BitShift ✅
BitwiseAnd Help wanted
BitwiseNot Help wanted
BitwiseOr Help wanted
BitwiseXor Help wanted
Cast ✅
Ceil ✅
Celu ✅
CenterCropPad Help wanted
Clip ✅
Col2Im Help wanted
Compress ✅
ConcatFromSequence ✅
Concat ✅
ConstantOfShape ✅
Constant ✅
Conv ✅
ConvTranspose ✅
Cosh ✅
Cos ✅
CumSum ✅
DepthToSpace ✅
Det ✅
DequantizeLinear ✅
DFT Help wanted
Div ✅
Dropout ✅
DynamicQuantizeLinear ✅
Einsum ✅
Elu ✅
Equal ✅
Erf ✅
Expand ✅
Exp ✅
EyeLike ✅
Flatten ✅
Floor ✅
FusedConv ✅
GatherElements ✅
GatherND ✅
Gather ✅
Gemm ✅
GlobalAveragePool ✅
GlobalLpPool ✅
GlobalMaxPool ✅
GreaterOrEqual ✅
Greater ✅
GridSample ✅
GroupNormalization Help wanted
GRU ✅
Hardmax ✅
HardSigmoid ✅
HardSwish ✅
Identity ✅
If ✅
Input ✅
InstanceNormalization ✅
Inverse ✅
IsInf ✅
IsNaN ✅
LayerNormalization ✅
LeakyRelu ✅
LessOrEqual ✅
Less ✅
Log ✅
LogSoftmax ✅
Loop Help wanted
LpNormalization ✅
LRN ✅
LSTM ✅
MatMul ✅
MatMulInteger ✅
MaxPool ✅
Max ✅
MaxRoiPool Help wanted
MaxUnpool ✅
Mean ✅
MeanVarianceNormalization ✅
MelWeightMatrix Help wanted
Min ✅
Mish ✅
Mod ✅
Mul ✅
Multinomial ✅
Neg ✅
NonMaxSuppression ✅
NonZero ✅
Optional Help wanted
OptionalGetElement Help wanted
OptionalHasElement Help wanted
Not ✅
OneHot ✅
Or ✅
Pad ✅
Pow ✅
PRelu ✅
QLinearAdd ✅
QLinearConcat ✅
QLinearConv ✅
QLinearLeakyRelu ✅
QLinearMatMul ✅
QLinearMul ✅
QLinearSigmoid ✅
QLinearSoftmax ✅
QuantizeLinear ✅
RandomNormalLike ✅
RandomNormal ✅
RandomUniformLike ✅
RandomUniform ✅
Range ✅
Reciprocal ✅
ReduceL1 ✅
ReduceL2 ✅
ReduceLogSum ✅
ReduceLogSumExp ✅
ReduceMax ✅
ReduceMean ✅
ReduceMin ✅
ReduceProd ✅
ReduceSum ✅
ReduceSumSquare ✅
Relu ✅
Reshape ✅
Resize ✅
ReverseSequence ✅
RNN ✅
RoiAlign ✅
Round ✅
ScaleAndTranslate ✅
Scatter ✅
ScatterElements ✅
ScatterND ✅
Scan Help wanted
Selu ✅
SequenceAt ✅
SequenceConstruct ✅
SequenceEmpty ✅
SequenceErase ✅
SequenceInsert ✅
SequenceLength ✅
Shape ✅
Shrink ✅
Sigmoid ✅
Sign ✅
Sinh ✅
Sin ✅
Size ✅
Slice ✅
Softmax ✅
Softplus ✅
Softsign ✅
SpaceToDepth ✅
Split ✅
SplitToSequence ✅
Sqrt ✅
Squeeze ✅
STFT Help wanted
StringNormalizer Help wanted
Sub ✅
Sum ✅
Tanh ✅
Tan ✅
TfIdfVectorizer Help wanted
ThresholdedRelu ✅
Tile ✅
TopK ✅
Transpose ✅
Trilu ✅
Unique ✅
Unsqueeze ✅
Upsample ✅
Where ✅
Xor ✅

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