Conversation
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/18152
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 New Failure, 1 Cancelled JobAs of commit 48f6862 with merge base 090af6c ( NEW FAILURE - The following job has failed:
CANCELLED JOB - The following job was cancelled. Please retry:
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This PR needs a
|
|
LGTM. Update the title to reflect adding example, and also CPU/XNNPACK tests. Thanks. |
|
Update the PR summary with description, and what's your Test Plan? |
just update ci |
Add DINOv2 image classification support for the ExecuTorch CUDA backend.
- Export: Python script to export
facebook/dinov2-small-imagenet1k-1-layer (a lightweight 1-layer DINOv2
ViT-S/14 variant) to .pte + .ptd with CUDA backend, supporting both
Linux and Windows targets via --backend cuda|cuda-windows
- C++ Runner: Image classification runner using stb\_image for
loading/resizing, ImageNet normalization, and bf16 input/output. Prints
top-k predictions with ImageNet class labels
Build: CMake + CMakePresets + Makefile target (make dinov2-cuda)
- CI: Integrated into cuda.yml workflow — exports model artifact and
runs e2e test with a real dog image, checking for expected output
("Samoyed")
Add DINOv2 image classification support for the ExecuTorch CUDA backend.
Build: CMake + CMakePresets + Makefile target (make dinov2-cuda)