pytorch-UNet | 2D and 3D UNet implementation in PyTorch | Machine Learning library

by cosmic-cortex Python Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | pytorch-UNet Summary

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

An example image from the Kaggle Data Science Bowl 2018:. This repository was created to. In essence, the U-Net is built up using encoder and decoder blocks, each of them consisting of convolutional and pooling layers. With this implementation, you can build your U-Net using the First, Encoder, Center, Decoder and Last blocks, controlling the complexity and the number of these blocks. (Because the first, last and the middle of these blocks are somewhat special, they require their own class.).

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Quality

Security

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Support

pytorch-UNet has a low active ecosystem.

It has 60 star(s) with 17 fork(s). There are 3 watchers for this library.

It had no major release in the last 6 months.

There are 11 open issues and 2 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of pytorch-UNet is current.

Quality

pytorch-UNet has 0 bugs and 0 code smells.

Security

pytorch-UNet has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

pytorch-UNet code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

pytorch-UNet is licensed under the MIT License. This license is Permissive.

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

Reuse

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

pytorch-UNet 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.

pytorch-UNet saves you 266 person hours of effort in developing the same functionality from scratch.

It has 646 lines of code, 59 functions and 10 files.

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

Top functions reviewed by kandi - BETA

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

Fit the model to a dataset
Fit a single epoch
Calculate evaluation of a single epoch
Predict dataset
Make directories if necessary
Return the results as a dictionary
Log data to the log
Reset the metrics
Write the logs to a csv file
Forward the encoder
Pad this array to a given shape
Creates a weighted metric based on the classwise_metric
Compute the encoder
Compute the classwise iourier iou value
Merge masks in masks folder

Get all kandi verified functions for this library.

pytorch-UNet Key Features

No Key Features are available at this moment for pytorch-UNet.

pytorch-UNet Examples and Code Snippets

No Code Snippets are available at this moment for pytorch-UNet.

Community Discussions

Trending Discussions on pytorch-UNet

Modify existing Pytorch code to run on multiple GPUs

Pytorch summary only works for one specific input size for U-Net

QUESTION

Modify existing Pytorch code to run on multiple GPUs

Asked 2020-Oct-03 at 23:52

I'm trying to run Pytoch UNet from the following link on 2 or more GPUs

Pytorch-UNet github

the changes the I did till now is:

1. from:

...

ANSWER

Answered 2020-Oct-03 at 23:52

My mistake was changing output = net(input) (commonly named as model) to:

output = net.module(input)

you can find information here

Source https://stackoverflow.com/questions/64185568

QUESTION

Pytorch summary only works for one specific input size for U-Net

Asked 2020-Feb-14 at 01:13

I am trying to implement the UNet architecture in Pytorch. When I print the model using print(model) I get the correct architecture:

but when I try to print the summary using (or any other input size for that matter):

...

ANSWER

Answered 2020-Feb-14 at 01:13

This UNet architecture you provided doesn't support that shape (unless the depth parameter is <= 3). Ultimately the reason for this is that the size of a downsampling operation isn't invertible since multiple input shapes map to the same output shape. For example consider

Source https://stackoverflow.com/questions/60199088

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

Vulnerabilities

No vulnerabilities reported

Install pytorch-UNet

The simplest way to use the implemented U-Net is with the provided train.py and predict.py scripts.

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 .

Find more information at: