Arm backend: Add RIFE PTQ/QAT accuracy flow#20974
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Add a Practical-RIFE PTQ/QAT comparison script under the Arm QAT example area. The flow loads original RIFE weights, uses real frame triples, preserves uint8 image IO by default, and writes JSON, CSV, Markdown, and quantization coverage reports. If a requested triples list is missing, create deterministic random data for smoke testing. The reports clearly label this as smoke-test data, not real accuracy data. Signed-off-by: Baris Demir <baris.demir@arm.com> Change-Id: Ia60c2a43a8f600405fda1b782965f560c69f945b
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/20974
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit debf330 with merge base 0f02e85 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
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Add a Practical-RIFE PTQ/QAT comparison script under the Arm QAT example area.
The flow loads original RIFE weights, uses real frame triples, preserves uint8 image IO by default, and writes JSON, CSV, Markdown, and quantisation coverage reports.
If a requested triples list is missing, create deterministic random data for smoke testing. The reports clearly label this as smoke-test data, not real accuracy data.
cc @digantdesai @freddan80 @per @zingo @oscarandersson8218 @mansnils @Sebastian-Larsson @robell @rascani