pro_gan_pytorch | Unofficial PyTorch implementation of the paper | Machine Learning library

by akanimax Python Version: v_1.2.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | pro_gan_pytorch Summary

pro_gan_pytorch is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. pro_gan_pytorch has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install pro_gan_pytorch' or download it from GitHub, PyPI.

Unofficial PyTorch implementation of Paper titled "Progressive growing of GANs for improved Quality, Stability, and Variation". For the official TensorFlow code, please refer to this repo.

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pro_gan_pytorch has a low active ecosystem.

It has 510 star(s) with 97 fork(s). There are 16 watchers for this library.

It had no major release in the last 12 months.

There are 1 open issues and 52 have been closed. On average issues are closed in 499 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pro_gan_pytorch is v_1.2.0

Quality

pro_gan_pytorch has 0 bugs and 0 code smells.

Security

pro_gan_pytorch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pro_gan_pytorch code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pro_gan_pytorch is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

pro_gan_pytorch releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

pro_gan_pytorch saves you 169 person hours of effort in developing the same functionality from scratch.

It has 420 lines of code, 37 functions and 5 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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pro_gan_pytorch Key Features

No Key Features are available at this moment for pro_gan_pytorch.

pro_gan_pytorch Examples and Code Snippets

No Code Snippets are available at this moment for pro_gan_pytorch.

Community Discussions

Trending Discussions on pro_gan_pytorch

PyTorch - convert ProGAN agent from pth to onnx

QUESTION

PyTorch - convert ProGAN agent from pth to onnx

Asked 2020-Mar-25 at 12:38

I trained a ProGAN agent using this PyTorch reimplementation, and I saved the agent as a .pth. Now I need to convert the agent into the .onnx format, which I am doing using this scipt:

...

ANSWER

Answered 2020-Mar-25 at 12:38

Files you have there are state_dict, which are simply mappings of layer name to tensor weights biases and a-like (see here for more thorough introduction).

What that means is that you need a model so those saved weights and biases can be mapped upon, but first things first:

1. Model preparation

Clone the repository where model definitions are located and open file /pro_gan_pytorch/pro_gan_pytorch/PRO_GAN.py. We need some modifications in order for it to work with onnx. onnx exporter requires input to be passed as torch.tensor only (or list/dict of those), while Generator class needs int and float arguments).

Simple solution it to slightly modify forward function (line 80 in the file, you can verify it on GitHub) to the following:

Source https://stackoverflow.com/questions/60845222

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install pro_gan_pytorch

You can install using 'pip install pro_gan_pytorch' or download it from GitHub, PyPI.
You can use pro_gan_pytorch like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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