https://github.com/tensorlayer/tlx2onnx
ONNX Model Exporter for TensorLayerX
https://github.com/tensorlayer/tlx2onnx
deep-learning mindspore neural-network onnx onnxruntime paddlepaddle python pytorch tensorflow tensorlayerx
Last synced: about 1 month ago
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ONNX Model Exporter for TensorLayerX
- Host: GitHub
- URL: https://github.com/tensorlayer/tlx2onnx
- Owner: tensorlayer
- License: apache-2.0
- Created: 2022-04-14T01:36:17.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-09-19T07:08:59.000Z (over 2 years ago)
- Last Synced: 2024-10-14T06:45:12.745Z (8 months ago)
- Topics: deep-learning, mindspore, neural-network, onnx, onnxruntime, paddlepaddle, python, pytorch, tensorflow, tensorlayerx
- Language: Python
- Homepage:
- Size: 178 KB
- Stars: 21
- Watchers: 6
- Forks: 1
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# TLX2ONNX
ONNX Model Exporter for TensorLayerX. It's updated on Both [OpenI](https://git.openi.org.cn/OpenI/TLX2ONNX) and [Github](https://github.com/tensorlayer/TLX2ONNX/). You can get a [free GPU](https://git.openi.org.cn/OpenI/TLX2ONNX/debugjob?debugListType=all) on OpenI to use this project.
## Introduction
TLX2ONNX enables users to convert models from TensorLayerX to ONNX.
- Supported operators. TLX2ONNX can stably export models to ONNX Opset 9~11, and partialy support lower version opset. More details please refer to [Operator list](OP_LIST.md).
- Supported Layers. You can find officially verified Layers by TLX2ONNX/tests in [TLX2ONNX/test](https://github.com/tensorlayer/TLX2ONNX/tree/main/tests).
## Installation
#### Via Pip
```bash
pip install tlx2onnx
```
#### From Source
```bash
git clone https://github.com/tensorlayer/TLX2ONNX.git
cd TLX2ONNX
python setup.py install
```
## Usage
TLX2ONNX can convert models built using TensorLayerX Module Subclass and Layers, and the Layers support list can be found in [Operator list](OP_LIST.md).
The following is an example of converting a multi-layer perceptron. You can get the code from [here](https://github.com/tensorlayer/TLX2ONNX/tree/main/tests/test_merge.py).
```python
import os
os.environ["TL_BACKEND"] = 'tensorflow'
import tensorlayerx as tlx
from tensorlayerx.nn import Module
from tensorlayerx.nn import Linear, Concat, Elementwise
from tlx2onnx.main import export
import onnxruntime as rt
import numpy as np
class CustomModel(Module):
def __init__(self):
super(CustomModel, self).__init__(name="custom")
self.linear1 = Linear(in_features=20, out_features=10, act=tlx.ReLU, name='relu1_1')
self.linear2 = Linear(in_features=20, out_features=10, act=tlx.ReLU, name='relu2_1')
self.concat = Concat(concat_dim=1, name='concat_layer')
def forward(self, inputs):
d1 = self.linear1(inputs)
d2 = self.linear2(inputs)
outputs = self.concat([d1, d2])
return outputs
net = CustomModel()
input = tlx.nn.Input(shape=(3, 20), init=tlx.initializers.RandomNormal())
net.set_eval()
output = net(input)
print("tlx out", output)
onnx_model = export(net, input_spec=input, path='concat.onnx')
# Infer Model
sess = rt.InferenceSession('concat.onnx')
input_name = sess.get_inputs()[0].name
output_name = sess.get_outputs()[0].name
input_data = np.array(input, dtype=np.float32)
result = sess.run([output_name], {input_name: input_data})
print('onnx out', result)
```
The converted onnx file can be viewed via [Netron](https://github.com/lutzroeder/netron).
The converted results have almost no loss of accuracy.
And the graph show the input and output sizes of each layer, which is very helpful for checking the model.
# Citation
If you find TensorLayerX or TLX2ONNX useful for your project, please cite the following papers：
```
@article{tensorlayer2017,
author = {Dong, Hao and Supratak, Akara and Mai, Luo and Liu, Fangde and Oehmichen, Axel and Yu, Simiao and Guo, Yike},
journal = {ACM Multimedia},
title = {{TensorLayer: A Versatile Library for Efficient Deep Learning Development}},
url = {http://tensorlayer.org},
year = {2017}
}
@inproceedings{tensorlayer2021,
title={TensorLayer 3.0: A Deep Learning Library Compatible With Multiple Backends},
author={Lai, Cheng and Han, Jiarong and Dong, Hao},
booktitle={2021 IEEE International Conference on Multimedia \& Expo Workshops (ICMEW)},
pages={1--3},
year={2021},
organization={IEEE}
}
```