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review.mlplatform.org / ml / armnn / 4631582a4c8b92917633d0af4ebcc8fff2abd04a
commit4631582a4c8b92917633d0af4ebcc8fff2abd04a[log] [tgz]
authorMatteo Martincigh <matteo.martincigh@arm.com>Wed Nov 28 16:22:36 2018 +0000
committerMatteo Martincigh <matteo.martincigh@arm.com>Wed Dec 05 13:48:43 2018 +0000
tree15f237e78af14394486699dedb834531af207067
parentc2130a070e6a9196d193c93a02b5f118810dd59a [diff]
IVGCVSW-2264 Remove input swizzling from ParseConv2D in the TF parser

 * Removed the input swizzling when the data layout is NHWC
 * Permuting weights depending on the data layout used
 * Added getter methods to ParsedConstTfOperation to get the tensor
   info and the storage memory area, needed for swizzling the weights
 * Added unit tests for both NHWC and NCHW data layouts

Change-Id: I6543900c594417df630b2663d8551158b93b7836
3 files changed
tree: 15f237e78af14394486699dedb834531af207067
  1. Android.bp
  2. Android.mk
  3. BuildGuideAndroidNDK.md
  4. BuildGuideCrossCompilation.md
  5. CMakeLists.txt
  6. ContributorGuide.md
  7. LICENSE
  8. README.md
  9. cmake/
  10. docs/
  11. include/
  12. samples/
  13. scripts/
  14. src/
  15. tests/
  16. third-party/
README.md

Arm NN

For more information about Arm NN, see: https://developer.arm.com/products/processors/machine-learning/arm-nn

There is a getting started guide here using TensorFlow: https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-tensorflow

There is a getting started guide here using TensorFlow Lite: https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-tensorflow-lite

There is a getting started guide here using Caffe: https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-caffe

There is a getting started guide here using ONNX: https://developer.arm.com/technologies/machine-learning-on-arm/developer-material/how-to-guides/configuring-the-arm-nn-sdk-build-environment-for-onnx

There is a guide for backend development: Backend development guide

Build Instructions

Arm tests the build system of Arm NN with the following build environments:

Arm NN is written using portable C++14 and the build system uses CMake so it is possible to build for a wide variety of target platforms, from a wide variety of host environments.

The armnn/tests directory contains tests used during ArmNN development. Many of them depend on third-party IP, model protobufs and image files not distributed with ArmNN. The dependencies of some of the tests are available freely on the Internet, for those who wish to experiment.

The 'ExecuteNetwork' program, in armnn/tests/ExecuteNetwork, has no additional dependencies beyond those required by ArmNN and the model parsers. It takes any model and any input tensor, and simply prints out the output tensor. Run with no arguments to see command-line help.

The 'armnn/samples' directory contains SimpleSample.cpp. A very basic example of the ArmNN SDK API in use.

Note that Arm NN needs to be built against a particular version of ARM's Compute Library. The get_compute_library.sh in the scripts subdirectory will clone the compute library from the review.mlplatform.org github repository into a directory alongside armnn named 'clframework' and checkouts the correct revision

License

Arm NN is provided under the MIT license. See LICENSE for more information. Contributions to this project are accepted under the same license.

Individual files contain the following tag instead of the full license text.

SPDX-License-Identifier: MIT

This enables machine processing of license information based on the SPDX License Identifiers that are available here: http://spdx.org/licenses/

Contributions

The ArmNN project welcomes contributions. Please see the Contributor Guide for more details.