onnx-tool

A tool for ONNX model:

Shape inference.
MACs(FLOPs) counting for each layer.
Extract subgraph from ONNX model, or do inplace op fusion. ...
and any operation you can image with ONNX.

New: Pytorch models are also valid if torch.onnx.export succeeded.

Shape inference

how to use: data/Profile.md.
pytorch usage: data/PytorchUsage.md.

MACs counting for each layer (FLOPs=2*MACs)

Float MultipleAdd Count, Memory Usage(in bytes), Parameters(elements number)

how to use: data/Profile.md.
pytorch usage: data/PytorchUsage.md.

Extract subgraph from ONNX model

how to use: data/Subgraph.md.

Inplace op fusion

how to use: data/Subgraph.md.

Add any hidden tensors to model's outputs

how to use: data/Profile.md.

Tensor operations

Export weight tensors to files
Simplify tensor and node names, convert name from a long string to a short string
Remove unused tensors, models like vgg19-7.onnx set its static weight tensors as its input tensors
Set custom input and output tensors' name and dimension, change model from fixed input to dynamic input
how to use: data/Tensors.md.

How to install

pip install onnx-tool

pip install --upgrade git+https://github.com/ThanatosShinji/onnx-tool.git

python>=3.6

If pip install onnx-tool failed by onnx's installation, you may try pip install onnx==1.8.1 (a lower version like this) first.
Then pip install onnx-tool again.

Known Issues

Loop op is not supported

Results of ONNX Model Zoo and SOTA models

Some models have dynamic input shapes. The MACs varies from input shapes. The input shapes used in these results are writen to data/public/config.py. These onnx models with all tensors' shape can be downloaded: baidu drive(code: p91k) google drive

Model	Params(M)	MACs(M)
MobileNet v2-1.0-fp32	3.3	300
ResNet50_fp32	25	3868
SqueezeNet 1.0	1.23	351
VGG 19	143.66	19643
AlexNet	60.96	665
GoogleNet	6.99	1606
googlenet_age_adience	5.98	1605
LResNet100E-IR	65.22	12102
BERT-Squad	113.61	22767
BiDAF	18.08	9.87
EfficientNet-Lite4	12.96	1361
Emotion FERPlus	12.95	877
Mask R-CNN R-50-FPN-fp32	46.77	92077

Model	Params(M)	MACs(M)
rvm_mobilenetv3_fp32.onnx	3.73	4289
yolov4	64.33	33019
ConvNeXt-L	229.79	34872
edgenext_small	5.58	1357
SSD	19.98	216598
RealESRGAN_x4plus.pth	16.69	73551
ShuffleNet-v2-fp32	2.29	146
GPT-2	137.02	1103
T5-encoder	109.62	686
T5-decoder-with-lm-head	162.62	1113
RoBERTa-BASE	124.64	688
Faster R-CNN R-50-FPN-fp32	44.10	46018
FCN ResNet-50	35.29	37056

Release files for onnx-tool 0.2.9

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for onnx-tool 0.2.9
File	Size	Uploaded
onnx-tool-0.2.9.tar.gz	23.1 kB	Sep 1, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for onnx-tool 0.2.9
File	Interpreter	ABI	Platform	Reset
onnx_tool-0.2.9-py3-none-any.whl 22.5 kB Sep 1, 2022	Python 3	none	any	Details

Total release size: 45.6 kB

Release files / onnx-tool-0.2.9.tar.gz

Download URL	onnx-tool-0.2.9.tar.gz
Size	23.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`6e1f2e82c4a4ee5223e5165ecc808401aaa2263b8495ada93d215775c187e8b6`
BLAKE2b-256 checksum How to use checksums	`860b07e89e3f4cd95e38bf180c656d7a0b6e43a439b4ac86899353e059a4ef4c`
Upload date	Sep 1, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.1 CPython/3.10.4`

Release files / onnx_tool-0.2.9-py3-none-any.whl

Download URL	onnx_tool-0.2.9-py3-none-any.whl
Size	22.5 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`a429213e5c2573914cbec2786e4020e9f5b1afd69a155c9b0f88bd09191a784a`
BLAKE2b-256 checksum How to use checksums	`366918c9e9574bb95226f877bb19092c94d87f120d7d7eace76e39c7fffa5847`
Upload date	Sep 1, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.1 CPython/3.10.4`