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Adding Models to Test Matrix for Zephyr CI Test #13193
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/13193
Note: Links to docs will display an error until the docs builds have been completed. ❌ 3 New Failures, 19 Pending, 1 Unrelated FailureAs of commit b2a355e with merge base f7f486d ( NEW FAILURES - The following jobs have failed:
BROKEN TRUNK - The following job failed but were present on the merge base:👉 Rebase onto the `viable/strict` branch to avoid these failures
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### Summary Previously, only the example Add model was included in the matrix of models that the Zephyr CI job tests. The Add model isn't very representative of real-world models, and may give a false sense of security on the functionality of incorporating ExecuTorch with Zephyr. This PR adds the simple `Softmax` and `Mobilenet V2` models to the test matrix. While the `Softmax` model is like the `Add` model (a toy example), `Mobilenet V2` includes using an image from the `imagenet` validation set, and verifies that the top predicted label matches expectation (i.e. predict "Samoyed" as best label). All three of these tests show example of this flow can be extend further for other models. Thus this PR integrates these test to ensure compatibility between ExecuTorch and Zephyr is maintained. ### Test plan Verified that these models work with the Zephyr ecosystem manually in a separate docker image.
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module: microcontrollers
For embedded MCUs like Cortex-M, or RTOS like Zephyr, does not track NPU backend like Arm Ethos.
release notes: none
Do not include this in the release notes
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Summary
Previously, only the example Add model was included in the matrix of models that the Zephyr CI job tests. The Add model isn't very representative of real-world models, and may give a false sense of security on the functionality of incorporating ExecuTorch with Zephyr. This PR adds the simple
SoftmaxandMobilenet V2models to the test matrix. While theSoftmaxmodel is like theAddmodel (a toy example),Mobilenet V2includes using an image from theimagenetvalidation set, and verifies that the top predicted label matches expectation (i.e. predict "Samoyed" as best label). All three of these tests show example of this flow can be extend further for other models. Thus this PR integrates these test to ensure compatibility between ExecuTorch and Zephyr is maintained.Test plan
Verified that these models work with the Zephyr ecosystem manually in a separate docker image.