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author | Gunes Bayir <gunes.bayir@arm.com> | 2023-09-28 10:30:18 +0100 |
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committer | Gunes Bayir <gunes.bayir@arm.com> | 2023-10-02 16:07:22 +0000 |
commit | c2a51bd2cc7c4148d9444e7377af44b2f6c264ba (patch) | |
tree | e8f66188d7e048a3f61d660c236ef66b33a0bf35 /arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h | |
parent | a396da19ee6e5c36ae07c11e4f16a6787e9bc143 (diff) | |
download | ComputeLibrary-c2a51bd2cc7c4148d9444e7377af44b2f6c264ba.tar.gz |
Optimize CL and Neon Winograd tests
Several test optimizations have been introduced into Winograd tests for Gpu and Cpu backends. The testing strategy has been detailed as a comment header in the test design files.
In summary
- Very large shapes in the nightly are made smaller
- If the underlying kernel is the same for different data types, we only need to stress some key aspects of the kernels (e.g. read/write lengths in case of fp32/fp16).
- In case the underlying kernel is the same (OpenCL), Fp16 is tested on a subset of the shapes
- In Cpu, there is no need to test every combination for both NCHW and NHWC as we just permute the inputs and use NHWC kernels anyways
- All activations does not need to be tested for each and every shape
Resolves: COMPMID-6464
Change-Id: Ie25fded85c65b9c7386dc21b23f9b695b1e77b07
Signed-off-by: Gunes Bayir <gunes.bayir@arm.com>
Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10393
Reviewed-by: Jakub Sujak <jakub.sujak@arm.com>
Comments-Addressed: Arm Jenkins <bsgcomp@arm.com>
Tested-by: Arm Jenkins <bsgcomp@arm.com>
Benchmark: Arm Jenkins <bsgcomp@arm.com>
Diffstat (limited to 'arm_compute/runtime/NEON/functions/NEGenerateProposalsLayer.h')
0 files changed, 0 insertions, 0 deletions