| commit | 6b0b53d307f956c3d67429f2a93df91f2eb2f483 | [log] [tgz] |
|---|---|---|
| author | jimfly01 <jim.flynn@arm.com> | Mon Oct 08 14:43:01 2018 +0100 |
| committer | Matthew Bentham <matthew.bentham@arm.com> | Wed Oct 10 16:16:58 2018 +0100 |
| tree | fcc03458f6396d9b9bee55674ca5af562ed5a16b | |
| parent | 0d9d4193200e8bfbaf4cb8f32c866d5fe73a3e7e [diff] |
IVGCVSW-1929: Fix for this defect (QASYM8 no scale) * Now hand in an optional vector of strings to Optimize function in which errors/warning messages are placed. * Optimize function changed to check outputs of each layer. If they are Quantized 8 bit but the scale has not been set an error message is added for each such output. * Unit test modelled on defect report added to exercise the fix. !android-nn-driver:152483 Change-Id: If9901f5324a516f1ab62858266b38f98dae16201
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/