dgcnn | Dynamic Graph CNN , which achieves state | Image Editing library

by WangYueFt Python Version: Current License: MIT

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

kandi X-RAY | dgcnn Summary

dgcnn is a Python library typically used in Media, Image Editing, Deep Learning applications. dgcnn has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. However dgcnn build file is not available. You can download it from GitHub.

DGCNN is the author's re-implementation of Dynamic Graph CNN, which achieves state-of-the-art performance on point-cloud-related high-level tasks including category classification, semantic segmentation and part segmentation. Further information please contact Yue Wang and Yongbin Sun.

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dgcnn has a medium active ecosystem.

It has 1409 star(s) with 402 fork(s). There are 25 watchers for this library.

It had no major release in the last 6 months.

There are 22 open issues and 57 have been closed. On average issues are closed in 124 days. There are 2 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of dgcnn is current.

Quality

dgcnn has 0 bugs and 0 code smells.

Security

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

dgcnn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

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

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

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

dgcnn 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.

It has 3557 lines of code, 216 functions and 23 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Write data to stream
Write the element list to the given stream
Pretty print text
Transpose input tensor
Convert bounding box labels to obj room
Collect the bounding box of the annotation files
Convert a bounding box label to a file
3d convolutional network
Batch norm for convolution
1d convolutional convolution layer
Compute the model
Collect point labels from annotation files
Convert angle to euler
Convert a matrix to Euler coordinates
Convert euler to angle
Convert from euler to Quaternion
Forward computation
Test the model
Convert a quaternion to euler
Convert a point label to an object
Load data for a given partition
Saves data with label and normal
Convert a batch of points into a single volume
Read binary data from stream
Convert a NumPy array to fields

Get all kandi verified functions for this library.

dgcnn Key Features

No Key Features are available at this moment for dgcnn.

dgcnn Examples and Code Snippets

No Code Snippets are available at this moment for dgcnn.

Community Discussions

Trending Discussions on dgcnn

Stellargraph failing to work with data shuffle

QUESTION

Stellargraph failing to work with data shuffle

Asked 2021-Jun-08 at 16:23

when I ran the StellarGraph's demo on graph classification using DGCNNs, I got the same result as in the demo.

However, when I tested what happens when I first shuffle the data using the following code:

...

ANSWER

Answered 2021-Jun-08 at 16:23

I found the problem. It wasn't anything to do with the shuffling algorithm, nor with StellarGraph's implementation. The problem was in the demo, at the following lines:

Source https://stackoverflow.com/questions/67779173

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

Vulnerabilities

No vulnerabilities reported

Install dgcnn

You can download it from GitHub.
You can use dgcnn 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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