pytorch2keras | PyTorch to Keras model convertor | Machine Learning library

by gmalivenko Python Version: v0.1.4 License: MIT

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

kandi X-RAY | pytorch2keras Summary

pytorch2keras is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Keras applications. pytorch2keras has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has medium support. You can download it from GitHub.

PyTorch to Keras model converter.

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pytorch2keras has a medium active ecosystem.

It has 836 star(s) with 141 fork(s). There are 15 watchers for this library.

It had no major release in the last 12 months.

There are 57 open issues and 69 have been closed. On average issues are closed in 81 days. There are 7 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch2keras is v0.1.4

Quality

pytorch2keras has 0 bugs and 0 code smells.

Security

pytorch2keras has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch2keras code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch2keras 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

pytorch2keras releases are available to install and integrate.

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

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

It has 2011 lines of code, 113 functions and 40 files.

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

Top functions reviewed by kandi - BETA

kandi has reviewed pytorch2keras and discovered the below as its top functions. This is intended to give you an instant insight into pytorch2keras implemented functionality, and help decide if they suit your requirements.

Convert a pytorch model to Keras .
Parse requirements file .

Get all kandi verified functions for this library.

pytorch2keras Key Features

No Key Features are available at this moment for pytorch2keras.

pytorch2keras Examples and Code Snippets

No Code Snippets are available at this moment for pytorch2keras.

Community Discussions

Trending Discussions on pytorch2keras

Channels first format in TensorFlow.js

QUESTION

Channels first format in TensorFlow.js

Asked 2021-Feb-15 at 15:01

I am converting https://github.com/Richard-An/StyleNAS to a browser compatible model. I am using TensorFlow.js. Due to some issues in the conversion libraries (https://github.com/nerox8664/pytorch2keras), I want to keep the model in channels_first (NCHW) format.

I know TensorFlow.js supports this. I can successfully import my model in the desired format. But I have a problem with data. When I convert the images to tensors via https://js.tensorflow.org/api/2.7.0/#browser.fromPixels they are always in channels_last (NHWC) format. Is there a way I can explicitly change this? I am new to TensorFlow.js and I couldn't find any answers for this.

Thanks

...

ANSWER

Answered 2021-Feb-15 at 15:01

The axis can be swapped

HWC -> CHW (for a 3d tensor)

Source https://stackoverflow.com/questions/66187164

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

Vulnerabilities

No vulnerabilities reported

Install pytorch2keras

You can download it from GitHub.
You can use pytorch2keras 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.

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Activations: ReLU LeakyReLU SELU Sigmoid Softmax TanhConstantsConvolutions: Conv2d ConvTrsnpose2dElement-wise: Add Mul Sub DivLinearNormalizations: BatchNorm2d InstanceNorm2dPoolings: MaxPool2d AvgPool2d Global MaxPool2d (adaptive pooling to shape [1, 1])

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