commit | 6f37f83a27160948fee366b9f195c52f78cb88f0 | [log] [tgz] |
---|---|---|
author | narpra01 <narumol.prangnawarat@arm.com> | Fri Dec 21 18:30:00 2018 +0000 |
committer | Les Bell <les.bell@arm.com> | Wed Jan 02 09:25:42 2019 +0000 |
tree | d09a8d5769c3ac2c8f45660d305e9a6124716310 | |
parent | c48ac8c8cea1748ebfef15144f070799d4a129c3 [diff] |
IVGCVSW-2353 Ignore control inputs in TensorFlow parser * Allow control inputs from TensorFlow graph but ignore them in ArmNN graph. * Add utility function to test ArmNN graph structure. * Add ArmNN graph structure tests in TensorFlow paresr to ensure that control inputs are ignored in ArmNN graph as well as their inputs that are not used anywhere else. Change-Id: Ib0ea0d2df85e3fc79b748fa4c9d20e0649352bc1
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
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
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/
The ArmNN project welcomes contributions. Please see the Contributor Guide for more details.