IM-NET | improved code for paper `` Learning Implicit Fields | 3D Printing library

by czq142857 Python Version: Current License: Non-SPDX

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

kandi X-RAY | IM-NET Summary

IM-NET is a Python library typically used in Modeling, 3D Printing, Deep Learning, Pytorch applications. IM-NET has no bugs, it has no vulnerabilities and it has low support. However IM-NET build file is not available and it has a Non-SPDX License. You can download it from GitHub.

The improved code for paper "Learning Implicit Fields for Generative Shape Modeling".

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Quality

Security

License

Reuse

Support

IM-NET has a low active ecosystem.

It has 116 star(s) with 21 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 15 have been closed. On average issues are closed in 4 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of IM-NET is current.

Quality

IM-NET has 0 bugs and 0 code smells.

Security

IM-NET has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

IM-NET code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

IM-NET 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

IM-NET releases are not available. You will need to build from source code and install.

IM-NET 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.

IM-NET saves you 1037 person hours of effort in developing the same functionality from scratch.

It has 2354 lines of code, 64 functions and 15 files.

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

Top functions reviewed by kandi - BETA

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

Train the model
Load pretrained checkpoint
Saves model to checkpoint directory
Build the model
Computes the tensorflow function
Compute the discriminator
Get points from voxel model
Sample a point in the cube
Test the model
Convert z - quadratic model to Voxel
Test the training
Loads checkpoint from given directory
Loads the image
Convert z - coordinates to voxel
Test the image
Run the test
Load tensorflow tensor
Evaluate the model for each batch
Returns the z - value of the tensor
List all images in root directory

Get all kandi verified functions for this library.

IM-NET Key Features

No Key Features are available at this moment for IM-NET.

IM-NET Examples and Code Snippets

No Code Snippets are available at this moment for IM-NET.

Community Discussions

Trending Discussions on IM-NET

Find patterns in a string containing a wildcard and escapes

QUESTION

Find patterns in a string containing a wildcard and escapes

Asked 2019-Feb-28 at 23:52

I searched the web now for hours and tried several alternatives, but couldn't find a satisfying solution. I have a string called tmp_txt containing several articles, which all start with

...

ANSWER

Answered 2019-Feb-28 at 16:37

It depends on how rigid the structure is before the date & title. You mention its different for the title, so it would be great if you could supply us some more strings in a vector, with the desired output titles required.

If it is consistent, you can use non-matching groups to remove the parts you are not interested in e.g.

Source https://stackoverflow.com/questions/54929971

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

Vulnerabilities

No vulnerabilities reported

Install IM-NET

You can download it from GitHub.
You can use IM-NET 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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