EnlightenGAN | IEEE TIP ] `` EnlightenGAN : Deep Light Enhancement | Machine Learning library

by VITA-Group Python Version: Current License: Non-SPDX

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

kandi X-RAY | EnlightenGAN Summary

EnlightenGAN is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Generative adversarial networks applications. EnlightenGAN has no bugs, it has no vulnerabilities and it has low support. However EnlightenGAN build file is not available and it has a Non-SPDX License. You can download it from GitHub.

IEEE Transaction on Image Processing, 2020, EnlightenGAN: Deep Light Enhancement without Paired Supervision.

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Quality

Security

License

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Support

EnlightenGAN has a low active ecosystem.

It has 746 star(s) with 193 fork(s). There are 17 watchers for this library.

It had no major release in the last 6 months.

There are 7 open issues and 106 have been closed. On average issues are closed in 46 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of EnlightenGAN is current.

Quality

EnlightenGAN has 0 bugs and 0 code smells.

Security

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

EnlightenGAN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

EnlightenGAN has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

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

EnlightenGAN 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.

EnlightenGAN saves you 2453 person hours of effort in developing the same functionality from scratch.

It has 5342 lines of code, 413 functions and 61 files.

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

Top functions reviewed by kandi - BETA

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

Forward computation
Pad a tensor
Display the current results
Add images
Create an instance of the model
Download data
Download data from a given dataset URL
Display the available options
Returns a list of tar zip files
Parse the options
Define the netG
Get a norm layer
Perform the prediction
The worker loop
Returns a list of transform options
Define the netD layer
Create a dataset
Patch the replication callback
Performs the data parallel operation on the slave
Saves visuals to the webpage
Saves images to the webpage
Optimize the parameters for a given epoch
Runs the forward prediction
Performs the prediction on the input_A
Predict from the input_A
Run the test function

Get all kandi verified functions for this library.

EnlightenGAN Key Features

No Key Features are available at this moment for EnlightenGAN.

EnlightenGAN Examples and Code Snippets

No Code Snippets are available at this moment for EnlightenGAN.

Community Discussions

Trending Discussions on EnlightenGAN

Difference In the output image when using traced model(.pt) with C++ and OpenCV

QUESTION

Difference In the output image when using traced model(.pt) with C++ and OpenCV

Asked 2021-Sep-17 at 16:25

I have retrained the model based on EnlightenGAN. Further I have traced the model in order to execute it in a C++ application using libTorch v1.6. However, I am getting slightly different results as compared to the python(executing the traced model) version.

The model requires the input RGB tensor and the attention map Image tensor as input. The attention map is basically to inform the model about the image region which requires contrast enhancement.

Below is the code to get inference the output from PT model in python.

...

ANSWER

Answered 2021-Sep-17 at 16:25

I found the problem in the above implementation. In the CPP version, I was not clamping the values after doing de-normalisation. I have put a clamping function and now it is working as expected. The edit part if anyone stumbles on the same problem is below:

Source https://stackoverflow.com/questions/69225244

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

Vulnerabilities

No vulnerabilities reported

Install EnlightenGAN

You can download it from GitHub.
You can use EnlightenGAN 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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