unisal | Unified Image and Video Saliency Modeling | Map library

by rdroste Python Version: Current License: Apache-2.0

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

kandi X-RAY | unisal Summary

unisal is a Python library typically used in Telecommunications, Media, Media, Entertainment, Geo, Map applications. unisal has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. However unisal build file is not available. You can download it from GitHub.

Unified Image and Video Saliency Modeling (ECCV 2020)

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Quality

Security

License

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Support

unisal has a low active ecosystem.

It has 105 star(s) with 33 fork(s). There are 7 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 119 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of unisal is current.

Quality

unisal has 0 bugs and 0 code smells.

Security

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

unisal code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

unisal is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

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

unisal 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.

unisal saves you 1345 person hours of effort in developing the same functionality from scratch.

It has 3014 lines of code, 196 functions and 10 files.

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

Top functions reviewed by kandi - BETA

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

Compute the backbone features
Make Gaussian maps
Return a list of gaussian maps
Log softmax
Run fine tuning
Exports all scalars
Train the model
Fit the model
Make a dropout connection
N_images dictionary
Get the data file for a given phase
Return the data file for a given vid
Load a dataset
Load a model from a directory
Get a sequence from a given vid
Return a dictionary of configuration parameters
N_images_dict
Measure model size
Generate predictions for sources
Measure the runtime
Forward pass through the layer
Loads jpeg files
Performs forward computation
Generate predictions for each example
Create a convolutional convolution layer
Get the optimizer

Get all kandi verified functions for this library.

unisal Key Features

No Key Features are available at this moment for unisal.

unisal Examples and Code Snippets

No Code Snippets are available at this moment for unisal.

Community Discussions

Trending Discussions on unisal

Pytorch CPU CUDA device load without gpu

QUESTION

Pytorch CPU CUDA device load without gpu

Asked 2021-Jun-11 at 12:41

I found this nice code Pytorch mobilenet which I cant get running on a CPU. https://github.com/rdroste/unisal

I am new to Pytorch so I am not shure what to do.

In line 174 of the module train.py the device is set:

...

ANSWER

Answered 2021-Jun-11 at 08:55

In https://pytorch.org/tutorials/beginner/saving_loading_models.html#save-on-gpu-load-on-cpu you'll see there's a map_location keyword argument to send weights to the proper device:

Source https://stackoverflow.com/questions/67934005

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

Vulnerabilities

No vulnerabilities reported

Install unisal

You can download it from GitHub.
You can use unisal 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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