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|
%global _empty_manifest_terminate_build 0
Name: python-onnx-simplifier
Version: 0.4.24
Release: 1
Summary: Simplify your ONNX model
License: Apache License v2.0
URL: https://github.com/daquexian/onnx-simplifier
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/a4/42/899654fae8596260e151375e2b6862e01583853a0b0c135478d59d2a891f/onnx-simplifier-0.4.24.tar.gz
Requires: python3-onnx
Requires: python3-rich
%description
# ONNX Simplifier
[](https://pypi.python.org/pypi/onnx-simplifier/)
[](https://pypi.python.org/pypi/onnx-simplifier/)
[](https://pypi.python.org/pypi/onnx-simplifier/)
[](https://github.com/daquexian/onnx-simplifier/pulls)
_ONNX is great, but sometimes too complicated._
## Background
One day I wanted to export the following simple reshape operation to ONNX:
```python
import torch
class JustReshape(torch.nn.Module):
def __init__(self):
super(JustReshape, self).__init__()
def forward(self, x):
return x.view((x.shape[0], x.shape[1], x.shape[3], x.shape[2]))
net = JustReshape()
model_name = 'just_reshape.onnx'
dummy_input = torch.randn(2, 3, 4, 5)
torch.onnx.export(net, dummy_input, model_name, input_names=['input'], output_names=['output'])
```
The input shape in this model is static, so what I expected is
However, I got the following complicated model instead:
## Our solution
ONNX Simplifier is presented to simplify the ONNX model. It infers the whole computation graph
and then replaces the redundant operators with their constant outputs (a.k.a. constant folding).
### Web version
We have published ONNX Simplifier on [convertmodel.com](https://www.convertmodel.com/#input=onnx&output=onnx). It works out of the box and **doesn't need any installation**. Note that it runs in the browser locally and your model is completely safe.
### Python version
```
pip3 install -U pip && pip3 install onnxsim
```
Then
```
onnxsim input_onnx_model output_onnx_model
```
For more advanced features, try the following command for help message
```
onnxsim -h
```
## Demonstration
An overall comparison between
[a complicated model](https://github.com/JDAI-CV/DNNLibrary/issues/17#issuecomment-455934190)
and its simplified version:
## In-script workflow
If you would like to embed ONNX simplifier python package in another script, it is just that simple.
```python
import onnx
from onnxsim import simplify
# load your predefined ONNX model
model = onnx.load(filename)
# convert model
model_simp, check = simplify(model)
assert check, "Simplified ONNX model could not be validated"
# use model_simp as a standard ONNX model object
```
You can see more details of the API in [onnxsim/onnx_simplifier.py](onnxsim/onnx_simplifier.py)
## Projects Using ONNX Simplifier
* [MXNet](https://mxnet.apache.org/versions/1.9.1/api/python/docs/tutorials/deploy/export/onnx.html#Simplify-the-exported-ONNX-model)
* [MMDetection](https://github.com/open-mmlab/mmdetection)
* [YOLOv5](https://github.com/ultralytics/yolov5)
* [ncnn](https://github.com/Tencent/ncnn)
* ...
## Chat
We created a Chinese QQ group for ONNX!
ONNX QQ Group (Chinese): 1021964010, verification code: nndab. Welcome to join!
For English users, I'm active on the [ONNX Slack](https://github.com/onnx/onnx#discuss). You can find and chat with me (daquexian) there.
%package -n python3-onnx-simplifier
Summary: Simplify your ONNX model
Provides: python-onnx-simplifier
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
BuildRequires: python3-cffi
BuildRequires: gcc
BuildRequires: gdb
%description -n python3-onnx-simplifier
# ONNX Simplifier
[](https://pypi.python.org/pypi/onnx-simplifier/)
[](https://pypi.python.org/pypi/onnx-simplifier/)
[](https://pypi.python.org/pypi/onnx-simplifier/)
[](https://github.com/daquexian/onnx-simplifier/pulls)
_ONNX is great, but sometimes too complicated._
## Background
One day I wanted to export the following simple reshape operation to ONNX:
```python
import torch
class JustReshape(torch.nn.Module):
def __init__(self):
super(JustReshape, self).__init__()
def forward(self, x):
return x.view((x.shape[0], x.shape[1], x.shape[3], x.shape[2]))
net = JustReshape()
model_name = 'just_reshape.onnx'
dummy_input = torch.randn(2, 3, 4, 5)
torch.onnx.export(net, dummy_input, model_name, input_names=['input'], output_names=['output'])
```
The input shape in this model is static, so what I expected is
However, I got the following complicated model instead:
## Our solution
ONNX Simplifier is presented to simplify the ONNX model. It infers the whole computation graph
and then replaces the redundant operators with their constant outputs (a.k.a. constant folding).
### Web version
We have published ONNX Simplifier on [convertmodel.com](https://www.convertmodel.com/#input=onnx&output=onnx). It works out of the box and **doesn't need any installation**. Note that it runs in the browser locally and your model is completely safe.
### Python version
```
pip3 install -U pip && pip3 install onnxsim
```
Then
```
onnxsim input_onnx_model output_onnx_model
```
For more advanced features, try the following command for help message
```
onnxsim -h
```
## Demonstration
An overall comparison between
[a complicated model](https://github.com/JDAI-CV/DNNLibrary/issues/17#issuecomment-455934190)
and its simplified version:
## In-script workflow
If you would like to embed ONNX simplifier python package in another script, it is just that simple.
```python
import onnx
from onnxsim import simplify
# load your predefined ONNX model
model = onnx.load(filename)
# convert model
model_simp, check = simplify(model)
assert check, "Simplified ONNX model could not be validated"
# use model_simp as a standard ONNX model object
```
You can see more details of the API in [onnxsim/onnx_simplifier.py](onnxsim/onnx_simplifier.py)
## Projects Using ONNX Simplifier
* [MXNet](https://mxnet.apache.org/versions/1.9.1/api/python/docs/tutorials/deploy/export/onnx.html#Simplify-the-exported-ONNX-model)
* [MMDetection](https://github.com/open-mmlab/mmdetection)
* [YOLOv5](https://github.com/ultralytics/yolov5)
* [ncnn](https://github.com/Tencent/ncnn)
* ...
## Chat
We created a Chinese QQ group for ONNX!
ONNX QQ Group (Chinese): 1021964010, verification code: nndab. Welcome to join!
For English users, I'm active on the [ONNX Slack](https://github.com/onnx/onnx#discuss). You can find and chat with me (daquexian) there.
%package help
Summary: Development documents and examples for onnx-simplifier
Provides: python3-onnx-simplifier-doc
%description help
# ONNX Simplifier
[](https://pypi.python.org/pypi/onnx-simplifier/)
[](https://pypi.python.org/pypi/onnx-simplifier/)
[](https://pypi.python.org/pypi/onnx-simplifier/)
[](https://github.com/daquexian/onnx-simplifier/pulls)
_ONNX is great, but sometimes too complicated._
## Background
One day I wanted to export the following simple reshape operation to ONNX:
```python
import torch
class JustReshape(torch.nn.Module):
def __init__(self):
super(JustReshape, self).__init__()
def forward(self, x):
return x.view((x.shape[0], x.shape[1], x.shape[3], x.shape[2]))
net = JustReshape()
model_name = 'just_reshape.onnx'
dummy_input = torch.randn(2, 3, 4, 5)
torch.onnx.export(net, dummy_input, model_name, input_names=['input'], output_names=['output'])
```
The input shape in this model is static, so what I expected is
However, I got the following complicated model instead:
## Our solution
ONNX Simplifier is presented to simplify the ONNX model. It infers the whole computation graph
and then replaces the redundant operators with their constant outputs (a.k.a. constant folding).
### Web version
We have published ONNX Simplifier on [convertmodel.com](https://www.convertmodel.com/#input=onnx&output=onnx). It works out of the box and **doesn't need any installation**. Note that it runs in the browser locally and your model is completely safe.
### Python version
```
pip3 install -U pip && pip3 install onnxsim
```
Then
```
onnxsim input_onnx_model output_onnx_model
```
For more advanced features, try the following command for help message
```
onnxsim -h
```
## Demonstration
An overall comparison between
[a complicated model](https://github.com/JDAI-CV/DNNLibrary/issues/17#issuecomment-455934190)
and its simplified version:
## In-script workflow
If you would like to embed ONNX simplifier python package in another script, it is just that simple.
```python
import onnx
from onnxsim import simplify
# load your predefined ONNX model
model = onnx.load(filename)
# convert model
model_simp, check = simplify(model)
assert check, "Simplified ONNX model could not be validated"
# use model_simp as a standard ONNX model object
```
You can see more details of the API in [onnxsim/onnx_simplifier.py](onnxsim/onnx_simplifier.py)
## Projects Using ONNX Simplifier
* [MXNet](https://mxnet.apache.org/versions/1.9.1/api/python/docs/tutorials/deploy/export/onnx.html#Simplify-the-exported-ONNX-model)
* [MMDetection](https://github.com/open-mmlab/mmdetection)
* [YOLOv5](https://github.com/ultralytics/yolov5)
* [ncnn](https://github.com/Tencent/ncnn)
* ...
## Chat
We created a Chinese QQ group for ONNX!
ONNX QQ Group (Chinese): 1021964010, verification code: nndab. Welcome to join!
For English users, I'm active on the [ONNX Slack](https://github.com/onnx/onnx#discuss). You can find and chat with me (daquexian) there.
%prep
%autosetup -n onnx-simplifier-0.4.24
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-onnx-simplifier -f filelist.lst
%dir %{python3_sitearch}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.4.24-1
- Package Spec generated
|
