e50b285c2c
* Added implementation for resize_area op. Initial commit. * Added implementation of resize_area op. Initial revision. Signed-off-by: shugeo <sgazeos@gmail.com> * Corrected resizeArea functor call. Signed-off-by: shugeo <sgazeos@gmail.com> * Implementation of resize_area. Cpu platform helpers. Signed-off-by: shugeo <sgazeos@gmail.com> * Implementation for resize_area helpers. The first part revision. Signed-off-by: shugeo <sgazeos@gmail.com> * Added a set of tests for resize_area op. Signed-off-by: shugeo <sgazeos@gmail.com> * Cuda implementation for resize_area. Initial approach. Signed-off-by: shugeo <sgazeos@gmail.com> * Adding multithreading for resize_area algorithm. Signed-off-by: shugeo <sgazeos@gmail.com> * Cuda implementation of resize_area helpers. Shared memory approach. Signed-off-by: shugeo <sgazeos@gmail.com> * Refactored resizeAreaKernel with cuda implementation. * Eliminated compilation errors. * ResizeArea helpers for cuda platform. The first working revision. Signed-off-by: shugeo <sgazeos@gmail.com> * Added test for batched resize_area op testing. Signed-off-by: shugeo <sgazeos@gmail.com> * Implementation of resize_are for cuda platform and tests. Signed-off-by: shugeo <sgazeos@gmail.com> * Fixed multithreading with resize_area op helper. Signed-off-by: shugeo <sgazeos@gmail.com> * Corrected copyright marks with sources. Signed-off-by: shugeo <sgazeos@gmail.com> * Corrected copyright mark for resize_area op implementation. Signed-off-by: shugeo <sgazeos@gmail.com> * Corrected copyright mark for parity ops header. Signed-off-by: shugeo <sgazeos@gmail.com> * Corrected typo in strings and so on with image resize ops. Signed-off-by: shugeo <sgazeos@gmail.com> * Refactored resize_area helpers and multithreading. Signed-off-by: shugeo <sgazeos@gmail.com> * Added ResizeArea wrapper * Added test with align_corners and fixed shape processing with only int args given for output size. Signed-off-by: shugeo <sgazeos@gmail.com> * Added test * TF mapping for ResizeArea * Fixed implementation issues with resize_area op for both platforms. Signed-off-by: shugeo <sgazeos@gmail.com> * Refactored image resizer struct to use flexible types for ints and floats. Signed-off-by: shugeo <sgazeos@gmail.com> * Improved multithreading with resizeAreaKernel launch. Signed-off-by: shugeo <sgazeos@gmail.com> * Use asynchronical memory copying with cuda platform image resize allocations. Signed-off-by: shugeo <sgazeos@gmail.com> Co-authored-by: Alexander Stoyakin <alexander.stoyakin@gmail.com> |
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ci | ||
contrib | ||
nd4j-backends | ||
nd4j-buffer | ||
nd4j-common | ||
nd4j-context | ||
nd4j-jdbc | ||
nd4j-parameter-server-parent | ||
nd4j-remote | ||
nd4j-serde | ||
nd4j-shade | ||
nd4j-tensorflow | ||
nd4j-uberjar | ||
.appveyor.yml | ||
.codeclimate.yml | ||
.gitignore | ||
.travis.yml | ||
LICENSE | ||
README.md | ||
RaspberryPi.md | ||
VERSION | ||
buildAllversions.sh | ||
buildmultiplescalaversions.sh | ||
pom.xml |
README.md
ND4J: Scientific Computing on the JVM
ND4J is an Apache 2.0-licensed scientific computing library for the JVM. By contributing code to this repository, you agree to make your contribution available under an Apache 2.0 license.
It is meant to be used in production environments rather than as a research tool, which means routines are designed to run fast with minimum RAM requirements.
Please search for the latest version on search.maven.org.
Or use the versions displayed in: https://github.com/eclipse/deeplearning4j-examples/blob/master/pom.xml
Main Features
- Versatile n-dimensional array object
- Multiplatform functionality including GPUs
- Linear algebra and signal processing functions
Specifics
- Supports GPUs via with the CUDA backend nd4j-cuda-7.5 and Native via nd4j-native.
- All of this is wrapped in a unifying interface.
- The API mimics the semantics of Numpy, Matlab and scikit-learn.
Documentation
Documentation is available at deeplearning4j.org. Access the JavaDocs for more detail.
Installation
To install ND4J, there are a couple of approaches, and more information can be found on the DL4J website.
Install from Maven Central
- Search for nd4j in the Maven Central Repository to find the available nd4j jars.
- Include the appropriate dependency in your pom.xml.
Clone from the GitHub Repo
https://deeplearning4j.org/docs/latest/deeplearning4j-build-from-source
Contribute
-
Check for open issues, or open a new issue to start a discussion around a feature idea or a bug.
-
If you feel uncomfortable or uncertain about an issue or your changes, feel free to contact us on Gitter using the link above.
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Fork the repository on GitHub to start making your changes to the master branch (or branch off of it).
-
Write a test, which shows that the bug was fixed or that the feature works as expected.
-
Note the repository follows the Google Java style with two modifications: 120-char column wrap and 4-spaces indentation. You can format your code to this format by typing
mvn formatter:format
in the subproject you work on, by using thecontrib/formatter.xml
at the root of the repository to configure the Eclipse formatter, or by using the INtellij plugin. -
Send a pull request, and bug us on Gitter until it gets merged and published.