roi_pooling | ROIPooling for pytorch | Machine Learning library

by escorciav Python Version: Current License: No License

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | roi_pooling Summary

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

ROIPooling for pytorch

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Quality

Security

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Reuse

Support

roi_pooling has a low active ecosystem.

It has 46 star(s) with 13 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 10 have been closed. On average issues are closed in 27 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of roi_pooling is current.

Quality

roi_pooling has 0 bugs and 0 code smells.

Security

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

roi_pooling code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

roi_pooling 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

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

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

Installation instructions, examples and code snippets are available.

roi_pooling saves you 140 person hours of effort in developing the same functionality from scratch.

It has 351 lines of code, 19 functions and 8 files.

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

Top functions reviewed by kandi - BETA

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

Convenience function for forward_forward_forward
Create a ROI pooling 2d
Return the type of Tensor
Load a CUDA kernel
Get number of BLOCKs
Backward computation
Create a ROI pooling2d
Forward 2D projection
Perform adaptive pooling on 2d

Get all kandi verified functions for this library.

roi_pooling Key Features

No Key Features are available at this moment for roi_pooling.

roi_pooling Examples and Code Snippets

No Code Snippets are available at this moment for roi_pooling.

Community Discussions

Trending Discussions on roi_pooling

How to fix ' ImportError: cannot import name 'numpy_type_map' ' in Python?

Roi pooling and backpropagation

coverting roi pooling in pytorch to nn layer

QUESTION

How to fix ' ImportError: cannot import name 'numpy_type_map' ' in Python?

Asked 2020-Jan-08 at 07:34

I've followed the instructions in Detectron and I've configured it several times: the code compiles as it should. When it comes to run the code, I get this error:

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ANSWER

Answered 2019-Jan-27 at 11:49

I suppose there is a version mismatch between detectron and the needed pytorch release you are using. if you look at latest pytorch source code, there is no numpy_type_map component. https://github.com/pytorch/pytorch/blob/master/torch/utils/data/dataloader.py

Source https://stackoverflow.com/questions/54387659

QUESTION

Roi pooling and backpropagation

Asked 2019-Dec-30 at 10:32

I have implemented ROI pooling at my graph. The code is as follows.

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ANSWER

Answered 2019-Dec-30 at 10:32

The issue was solved by putting conv layers after RoiPooling.

The first graph was used only for feature extraction using RoiPooling. RoiPooling output size was set bigger dimensions. Then those outputs were used as inputs to the second graph. There conv layers were placed. So that I have weights to optimize.

The modified graph is shown below.

Source https://stackoverflow.com/questions/59516770

QUESTION

coverting roi pooling in pytorch to nn layer

Asked 2018-Nov-05 at 18:29

I have a an mlmodel using ROI pooling for which I am using this (adapted from here) (non NN layer version)

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ANSWER

Answered 2018-Nov-05 at 18:29

Found the issue - The rois after multiplication with spatial scale were being rounded down and had to call round function before calling long like so

Source https://stackoverflow.com/questions/53157978

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

Vulnerabilities

No vulnerabilities reported

Install roi_pooling

We need the following requirements cuda, pytorch==1.0.1, cupy=5.1.0 which we can get most of them from anaconda.org with trusted channels.
Install anaconda or miniconda. Skip this if you already have miniconda or anaconda installed in your system.
Create a new environment conda create -n pytorch-extensions python=3.7 pytorch cupy -c pytorch This step creates a conda environment called pytorch-extensions. In case, you change the name keep it mind to update the next lines accordingly.
conda activate pytorch-extensions
python example.py Hopefully everything runs like the breeze.

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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