cfgnode | KSP ConfigNode parser | Parser library

by taniwha Python Version: Current License: GPL-2.0

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

kandi X-RAY | cfgnode Summary

cfgnode is a Python library typically used in Utilities, Parser applications. cfgnode has no bugs, it has no vulnerabilities, it has a Strong Copyleft License and it has low support. However cfgnode build file is not available. You can download it from GitHub.

cfgnode

Support

Quality

Security

License

Reuse

Support

cfgnode has a low active ecosystem.

It has 4 star(s) with 3 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of cfgnode is current.

Quality

cfgnode has 0 bugs and 0 code smells.

Security

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

cfgnode code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

cfgnode is licensed under the GPL-2.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

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

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

It has 1462 lines of code, 76 functions and 12 files.

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

Top functions reviewed by kandi - BETA

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

Find parts of a given path
Load from file
Returns the list of nodes that match the given key
Retrieves the value for the given key
Adds a new configuration node
Find engines
Returns a list of values corresponding to the given key
Get a node by key
Return a list of engine modules
Validate enum values
Retrieves all the nodes matching the given key
Load from a file
Validate that value is a positive float
Validate that value is a positive integer
Validate a vector value
Validate a quaternion
Check all the resources in vessel
Check for special cases
Recursively recursively all files under path
Check attach rules
Return a function that checks file extensions
Find resources defined by path
Collects all available engines
Find the ports in the vessel
Find statics in the given path
Validate a color
Check all nodes in node
Parse PART node
Returns the value associated with the given key

Get all kandi verified functions for this library.

cfgnode Key Features

No Key Features are available at this moment for cfgnode.

cfgnode Examples and Code Snippets

No Code Snippets are available at this moment for cfgnode.

Community Discussions

Trending Discussions on cfgnode

How to prune a Detectron2 model?

QUESTION

How to prune a Detectron2 model?

Asked 2020-Sep-03 at 08:41

I'm a teacher who is studying computer vision for months. I was very excited when I was able to train my first object detection model using Detectron2's Faster R-CNN model. And it works like a charm! Super cool!

But the problem is that, in order to increase the accuracy, I used the largest model in the model zoo.

Now I want to deploy this as something people can use to ease their job. But, the model is so large that it takes ~10 seconds to infer a single image on my CPU which is Intel i7-8750h.

Therefore, it's really difficult to deploy this model even on a regular cloud server. I need to use either GPU servers or latest model CPU servers which are really expensive and I'm not sure if I can even compensate for server expenses for months.

I need to make it smaller and faster for deployment.

So, yesterday I found that there's something like pruning the model!! I was very excited (since I'm not a computer or data scientists, don't blame me (((: )

I read official pruning documentation of PyTorch, but it's really difficult for me to understand.

I found global pruning is of the easiest one to do.

But the problem is, I have no idea what parameters should I write to prune.

Like I said, I used Faster R-CNN X-101 model. I have it as "model_final.pth". And it uses Base RCNN FPN.yaml and its meta architecture is "GeneralizedRCNN".

It seems like an easy configuration to do. But like I said, since it's not my field it's very hard for a person like me.

I'd be more than happy if you could help me on this step by step.

I'm leaving my cfg.yaml which I used training the model and I saved it using "dump" method in Detectron2 config class just in case. Here's the Drive link.

Thank you very much in advance.

...

ANSWER

Answered 2020-Sep-03 at 08:41

So I guess, you are trying to optimize inference time and achieving satisfactory accuracy. Without knowing details about your object types, training size, image size, it will be hard to provide suggestions. However, as you know, ML project development is an iterative process, you can have a look at the following page and check inference and accuracy.

https://github.com/facebookresearch/detectron2/blob/master/MODEL_ZOO.md#coco-object-detection-baselines

I would suggest, you try R50-FPN backbone and see how your accuracy comes. Then, you will get a better understanding of what to do next.

Source https://stackoverflow.com/questions/63687033

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

Vulnerabilities

No vulnerabilities reported

Install cfgnode

You can download it from GitHub.
You can use cfgnode 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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