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android / platform / external / armnn / db2b160b
commitdb2b160bf9e7759d0157dfa57ee940290f5170e3[log] [tgz]
authornarpra01 <narumol.prangnawarat@arm.com>Wed Jan 23 15:23:11 2019 +0000
committerNarumol Prangnawarat <narumol.prangnawarat@arm.com>Wed Jan 23 17:13:15 2019 +0000
tree536fa36ebc9eb8442b96b486a10cadab28d32647
parentc625f000198218fc8d03130ee5658f73b94b2683 [diff]
IVGCVSW-2511 Add end to end Gather layer test

 * Add end to end test for Gather operator
 * Add Support for int32 to Constant layer for Ref
 * Add Int32Workload
 * Add RefConstantWorkload as template for float, uint8, int32
 * Remove unused RefBaseConstantWorkload
 * Remove unused RefConstantFloat32Workload
 * Remove unused RefConstantUint8Workload
 * Add support check for int32 in LayerSupport functions

Change-Id: Ic970588a49ebe2aafb12be8adef52371feacaa7b
20 files changed
tree: 536fa36ebc9eb8442b96b486a10cadab28d32647
  1. cmake/
  2. docs/
  3. include/
  4. samples/
  5. scripts/
  6. src/
  7. tests/
  8. third-party/
  9. Android.bp
  10. Android.mk
  11. BuildGuideAndroidNDK.md
  12. BuildGuideCrossCompilation.md
  13. CMakeLists.txt
  14. ContributorGuide.md
  15. LICENSE
  16. README.md
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.