https://github.com/icgy96/model-pivot

Model-Pivot is a model conversion and visualization tool to help users inter-operate among different deep learning frameworks. Convert models between PyTorch and Tensorflow.
https://github.com/icgy96/model-pivot

intermediate-representation open-exchange pytorch tensorflow

Last synced: 19 days ago
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Model-Pivot is a model conversion and visualization tool to help users inter-operate among different deep learning frameworks. Convert models between PyTorch and Tensorflow.

• Host: GitHub
• URL: https://github.com/icgy96/model-pivot
• Owner: iCGY96
• License: mit
• Created: 2018-12-06T14:14:58.000Z (almost 6 years ago)
• Default Branch: master
• Last Pushed: 2021-12-11T09:13:54.000Z (almost 3 years ago)
• Last Synced: 2023-03-04T19:50:56.752Z (over 1 year ago)
• Topics: intermediate-representation, open-exchange, pytorch, tensorflow
• Language: Python
• Homepage: https://model-pivot.readthedocs.io
• Size: 1.91 MB
• Stars: 6
• Watchers: 2
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Model-Pivot

Model-Pivot is a model conversion and visualization tool to help users inter-operate among different deep learning frameworks. Convert models between PyTorch and Tensorflow.

IR is based on the National Information Technology Standardization ```Neural Network Representation and Model Compression Part 1: Convolution Neural Network``.

## Requirments

- tensorflow==1.8.0

- pytorch==0.4.0

- torchvision==0.2.0

- protobuf>=3.6.1

- python>=3.6

- flask

## How to deploy visualization on Web

If you want to access the deployed web page from an external network, you should first modify the *host* and *port* for the *./visualization/app.py* file.

You can deploy it on Web by running:

```shell

python app.py

```

## Model

Framework | ResNet50 | Inception V3 | ShuffleNet | FCN | LSTM |

:----------:|:----------:|:----------:|:----------:|:----------:|:----------:|

TensorFlow | √ | √ | √ | √ | √ |

PyTorch | √ | √ | √ | √ | √ | 

## Test for Tensorflow and PyTorch

```shell

CUDA_VSIBLE_DEVICES=0 python test.py

```