mrnet | The Multicast/Reduction Network library

by dyninst C Version: v5.0.1 License: Non-SPDX

X-Ray Key Features Code Snippets(1)Community Discussions(5)Vulnerabilities Install Support

kandi X-RAY | mrnet Summary

mrnet is a C library. mrnet has no bugs, it has no vulnerabilities and it has low support. However mrnet has a Non-SPDX License. You can download it from GitHub.

mrnet: a multicast/reduction network copyright 2003-2016 dorian c. arnold, philip c. roth, barton p. miller. license contains the usage terms for mrnet. mrnet is a customizable, high-performance software infrastructure for building scalable tools and applications. it supports efficient multicast and data aggregation functionality using a tree of processes between the tool's front-end and back-ends. mrnet-based tools may use these internal processes to distribute many important tool activities, for example to reduce data analysis time and keep tool front-end loads manageable. mrnet-based tool components communicate across logical channels called streams. at mrnet internal processes, filters are bound to these streams to synchronize and aggregate dataflows. using filters, mrnet can efficiently compute averages, sums, and other more complex aggregations

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Support

mrnet has a low active ecosystem.

It has 5 star(s) with 3 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 0 open issues and 3 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of mrnet is v5.0.1

Quality

mrnet has no bugs reported.

Security

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

License

mrnet 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

mrnet releases are available to install and integrate.

Installation instructions are not available. Examples and code snippets are available.

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mrnet Key Features

No Key Features are available at this moment for mrnet.

mrnet Examples and Code Snippets

default

Lines of Code : 17

License : Non-SPDX (NOASSERTION)

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* Scalable, flexible data aggregation:  MRNet provides a set of built-in
  filters for traditional data aggregation operations like averages, sums,
  and data concatenation.  MRNet also supports custom, dynamically-loaded
  filters for tool-specific

Community Discussions

Trending Discussions on mrnet

numpy array type not supported?

Converting DICOM image to numpy array of shape (s, 3, 256, 256)

ValueError: only one element tensors can be converted to Python scalars

EOFError: Ran out of input (CNN model)

Can't parse arguments (deep learning tutorial using pytorch)

QUESTION

numpy array type not supported?

Asked 2020-Jul-24 at 14:58

I'm trying to copy a model I was able to follow and run through a tutorial, but this time with my own data.

I was able to convert my own MRI images to numpy arrays in the same dimensions as the arrays the tutorial data is.

I tried replacing the numpy arrays in my tutorial with my own arrays and writing my own fictional csv file for normal or abnormal (case, not case).

However when I run it, I get:

...

ANSWER

Answered 2020-Jul-24 at 14:58

You can redefining the variable with astype

Source https://stackoverflow.com/questions/63075914

QUESTION

Converting DICOM image to numpy array of shape (s, 3, 256, 256)

Asked 2020-Jul-24 at 13:34

I've got folders with MRI images in them and I'm trying to replicate the MRnet study with my own data. Their model works on 1 .npy file per subject, shape (s, 3, 256, 256), with s being number of slices for a given subject (varies between subjects).

I've looked at several different methods of solving this but none seems to work for me. Closest I have gotten was to at least convert the .dcm files to JPEG using:

...

ANSWER

Answered 2020-Jul-24 at 13:34

you could modify this section of your code:

Source https://stackoverflow.com/questions/63071607

QUESTION

ValueError: only one element tensors can be converted to Python scalars

Asked 2020-Jul-19 at 10:06

I'm following this tutorial.

I'm at the last part where we combine the models in a regression.

I'm coding this in jupyter as follows:

...

ANSWER

Answered 2020-Jul-19 at 09:49

Only a tensor that contains a single value can be converted to a scalar with item(), try printing the contents of prediction, I imagine this is a vector of probabilities indicating which label is most likely. Using argmax on prediction will give you your actual predicted label (assuming your labels are 0-n).

Source https://stackoverflow.com/questions/62978582

QUESTION

EOFError: Ran out of input (CNN model)

Asked 2020-Jul-16 at 10:38

I'm following a tutorial on how to build CNN's for MRI.

I'm attempting to train the model myself and I've got a "data" folder in the folder where the files are (downloaded from his github) but when I try to train the model I get an error.

I run this in a CMD: python train.py -t acl -p sagittal --epochs=20 --prefix_name=one

However it returns:

...

ANSWER

Answered 2020-Jul-16 at 10:38

Change lines 177 and 182 of train.py from num_workers=11 to num_workers=0 . Windows has limit on the Pickle loader.

Source https://stackoverflow.com/questions/62930669

QUESTION

Can't parse arguments (deep learning tutorial using pytorch)

Asked 2020-Jul-15 at 00:30

I'm following this tutorial.

The first scripts run fine and I have a "data" folder in the folder of my scripts containing the MRI data downloaded from MRnet.

However when it comes to the "train" script I get an error. Here's the full script and the error (using jupyter notebook):

...

ANSWER

Answered 2020-Jul-15 at 00:30

I will guess.

ArgumentParser was created to get arguments when you run it in console/terminal not Juputer

Source https://stackoverflow.com/questions/62904639

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

Vulnerabilities

No vulnerabilities reported

Install mrnet

You can download it from GitHub.

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