dc0036f2c6
* Added implementation files for image_resize and resize_bicubic ops. * Image resize and image.resize_bicubic ops implementation. Initial revision. * Finished with infrastructure development for image.resize_bilinear op and image_resizo op implementation. * Refactored resize methods. * Added processing for Mitchelcubic algorithm. * Added check for input/output sizes. * Added int and float types for crop_and_resize op. * Refactored crop_and_resize output type check. * Added helper for bicubic interpolation as TF v.1 does. * Added TF v.1 bicubic helper for cuda platform. * Added cached class for bicubic algorithm. * Refactored cuda implementation for crop_and_resize helper to use proper output type. * Added facilities for bicubic interpolation. * Portion bicubic interpolation from TF. * Added tests for resize_bilinear testing. * Working implementation of bicubic interpolation and tests. * Refactored routines with image_resize bicubic op helper. * Refactored code with coding standards. * Refactored cpu helpers for resize_bicubic op. * Refactored bicubic helpers. * Added bicubic resize facilities. * Implementing cuda kernels for bicubic interpolation. Implementation step. * Cuda implementation of resize_bicubic op helper. * Refactor image.resize_bicubic op helpers. * Refactored helpers for resize_bicubic. Added error checking with cuda implementation. * Refactored cuda implementation of resize_bicubic op helper. The first working revision. * Cuda arch implementation for resize_bicubic op helper. Full working single-threaded revision. * Intermediate bicubic interpolation helper for cuda. * Refactored cpu helper for resize_bicubic. * Multithreaded cuda implementation for resize_bicubic. * Fixed merge issues. * Refactored nlp helpers. * Replicated resize_bicubic for 3D also. * Eliminated waste comments of unused code. * Eliminated waste comments with unused code. * Eliminated waste template definitions. * Eliminated waste debug code. * Eliminated waste comments. * Fixed multithreading with helpers. * Fixed test suites for float and double in float point input lists. * Fixed usage of reshape with 3D/4D on resizes. * Final fixes. * Fixed resize_neighbor op problem. |
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|---|---|---|
| .github | ||
| arbiter | ||
| datavec | ||
| deeplearning4j | ||
| docs | ||
| gym-java-client | ||
| jumpy | ||
| libnd4j | ||
| nd4j | ||
| nd4s | ||
| pydatavec | ||
| pydl4j | ||
| rl4j | ||
| scalnet | ||
| .gitignore | ||
| CONTRIBUTING.md | ||
| Jenkinsfile | ||
| LICENSE | ||
| README.md | ||
| change-cuda-versions.sh | ||
| change-scala-versions.sh | ||
| perform-release.sh | ||
| pom.xml | ||
README.md
Monorepo of Deeplearning4j
Welcome to the new monorepo of Deeplearning4j that contains the source code for all the following projects, in addition to the original repository of Deeplearning4j moved to deeplearning4j:
- https://github.com/eclipse/deeplearning4j/tree/master/libnd4j
- https://github.com/eclipse/deeplearning4j/tree/master/nd4j
- https://github.com/eclipse/deeplearning4j/tree/master/datavec
- https://github.com/eclipse/deeplearning4j/tree/master/arbiter
- https://github.com/eclipse/deeplearning4j/tree/master/nd4s
- https://github.com/eclipse/deeplearning4j/tree/master/gym-java-client
- https://github.com/eclipse/deeplearning4j/tree/master/rl4j
- https://github.com/eclipse/deeplearning4j/tree/master/scalnet
- https://github.com/eclipse/deeplearning4j/tree/master/pydl4j
- https://github.com/eclipse/deeplearning4j/tree/master/jumpy
- https://github.com/eclipse/deeplearning4j/tree/master/pydatavec
To build everything, we can use commands like
./change-cuda-versions.sh x.x
./change-scala-versions.sh 2.xx
./change-spark-versions.sh x
mvn clean install -Dmaven.test.skip -Dlibnd4j.cuda=x.x -Dlibnd4j.compute=xx
or
mvn -B -V -U clean install -pl '!jumpy,!pydatavec,!pydl4j' -Dlibnd4j.platform=linux-x86_64 -Dlibnd4j.chip=cuda -Dlibnd4j.cuda=9.2 -Dlibnd4j.compute=<your GPU CC> -Djavacpp.platform=linux-x86_64 -Dmaven.test.skip=true
An example of GPU "CC" or compute capability is 61 for Titan X Pascal.
Want some examples?
We have separate repository with various examples available: https://github.com/eclipse/deeplearning4j-examples
In the examples repo, you'll also find a tutorial series in Zeppelin: https://github.com/eclipse/deeplearning4j-examples/tree/master/tutorials