commit | 6b9658239d377372523fe49c71fde31701d986e3 | [log] [tgz] |
---|---|---|
author | James Conroy <james.conroy@arm.com> | Thu Nov 01 11:33:09 2018 +0000 |
committer | Narumol Prangnawarat <narumol.prangnawarat@arm.com> | Thu Nov 01 15:45:50 2018 +0000 |
tree | 0b14b3e81a1321c8e5e85d2800a6e969d6cb724f | |
parent | b9c8963c3d393baf27edf37ab732fa76ee53af50 [diff] |
IVGCVSW-2103: Add 2-Channel unit tests ResizeBilinear * Modifies ResizeBilinear unit tests to use 2-Channel tensor shapes for input and output data, to improve test coverage when exercising NHWC data layout. * Refactors unit tests to permute input and output data when exercising NHWC data layout. Change-Id: Ib7fb438cac23e78ff0104c895c3b7596bf7c3aa7
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: TensorFlow Lite Support
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: ONNX Support
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.
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/