StyleTransfer | PyTorch image deep style transfer library | Computer Vision library

by AlenUbuntu Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | StyleTransfer Summary

StyleTransfer is a Python library typically used in Artificial Intelligence, Computer Vision, Deep Learning, Pytorch, Tensorflow applications. StyleTransfer has no bugs, it has no vulnerabilities and it has low support. However StyleTransfer build file is not available. You can download it from GitHub.

This is an PyTorch image deep style transfer library. It provies implementations of current SOTA algorithms, including. The original implementations can be found at AdaIN, WCT, LST and FSP. With this library, as long as you can find your desired style images on web, you can edit your content image with different transferring effects.

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Quality

Security

License

Reuse

Support

StyleTransfer has a low active ecosystem.

It has 7 star(s) with 1 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

StyleTransfer has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of StyleTransfer is current.

Quality

StyleTransfer has no bugs reported.

Security

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

License

StyleTransfer does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

StyleTransfer releases are not available. You will need to build from source code and install.

StyleTransfer has no build file. You will be need to create the build yourself to build the component from source.

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

Top functions reviewed by kandi - BETA

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

Main train function
Get the full path to the input files
Calculate the content and style loss
Get a model by name
Forward the image with the given mask
Calculate the weight of a contig
Transform image with mask
Main function for train
Calculate loss
Forward feature with given style
Main function
Forward a feature
Load an image file
Convert two image data
Process the image
Compute the laplacian
Loads the decoder model weights
Return the path to the decoder model
Convert two images
Compare a key in a dict
Compute the style
Import all symbols
Loads the model
Compute the loss function
Transform the image
Compute the feature with the given style
Forward transform the image

Get all kandi verified functions for this library.

StyleTransfer Key Features

No Key Features are available at this moment for StyleTransfer.

StyleTransfer Examples and Code Snippets

No Code Snippets are available at this moment for StyleTransfer.

Community Discussions

Trending Discussions on StyleTransfer

How to convert from scipy.misc.imresize to imageio

torch.nn has no attribute named upsample

QUESTION

How to convert from scipy.misc.imresize to imageio

Asked 2021-Jan-20 at 17:00

Hi I'm running a slightly expensive aws... And trying to solve old scipy.imread to the new imagio.read standard.

In this file https://github.com/ml5js/training-styletransfer/blob/master/src/utils.py

...

ANSWER

Answered 2021-Jan-20 at 17:00

imresize is not part of scipy anymore. You can either downgrade to scipy i.e. 1.2.1 or install scikit-image and call skimage.transform.resize instead

Source https://stackoverflow.com/questions/65541077

QUESTION

torch.nn has no attribute named upsample

Asked 2017-Dec-04 at 16:45

Following this tutorial: https://www.digitalocean.com/community/tutorials/how-to-perform-neural-style-transfer-with-python-3-and-pytorch#step-2-%E2%80%94-running-your-first-style-transfer-experiment

When I run the example in Jupyter notebook, I get the following:

So, I've tried troubleshooting, which eventually got me to running it as per the github example (https://github.com/zhanghang1989/PyTorch-Multi-Style-Transfer) says to via command line:

...

ANSWER

Answered 2017-Dec-04 at 16:45

I think the reason maybe that you have an older version of PyTorch on your system. On my system, the pytorch version is 0.2.0, torch.nn has a module called Upsample.

You can uninstall your current version of pytorch and reinstall it.

Source https://stackoverflow.com/questions/47635918

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

Vulnerabilities

No vulnerabilities reported

Install StyleTransfer

You can download it from GitHub.
You can use StyleTransfer 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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