Qualcomm AI Engine Direct - Add depth_anything_v2_small to oss_scripts#18657
Qualcomm AI Engine Direct - Add depth_anything_v2_small to oss_scripts#18657zhaoxul-qti wants to merge 1 commit intopytorch:mainfrom
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Summary - Enable **depth_anything_v2_small** model (https://github.com/DepthAnything/Depth-Anything-V2) on HTP and integrates the script into `oss_scripts/`. - Provide `--dump_example_output` flag to dump the example image and export depth‑estimation images from both source model and QNN outputs. - Add unit tests to cover the added model. Test plan - Test with random images from ImageNet: `python examples/qualcomm/oss_scripts/depthanything_v2_small.py -a $ARTIFACT -d $IMAGENET_FOLDER_PATH -b build-android/ -H $HOST_NAME -s $DEVICE_ID -m $SOC_ID --seed 1126` - Test with the example image and export the post-processed source model output and QNN output into depth-estimation images: `python examples/qualcomm/oss_scripts/depthanything_v2_small.py -a $ARTIFACT -d $IMAGENET_FOLDER_PATH -b build-android/ -H $HOST_NAME -s $DEVICE_ID -m $SOC_ID --dump_example_output`
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Hi @cccclai, @abhinaykukkadapu, This PR is to add the model depth_anything_v2_small to qualcomm/oss_scripts. Would you mind helping to take a look? Thanks, |
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@zhaoxul-qti looks great, thanks for adding the support. |
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Hi @abhinaykukkadapu, |
Summary
oss_scripts/.--dump_example_outputflag to dump the example image and export depth‑estimation images from both source model and QNN outputs.Notes
Constraints
Test plan
Test with random images from ImageNet:
python examples/qualcomm/oss_scripts/depthanything_v2_small.py -a $ARTIFACT -d $IMAGENET_FOLDER_PATH -b build-android/ -H $HOST_NAME -s $DEVICE_ID -m $SOC_ID --seed 1126Test with the example image and export the post-processed source model output and QNN output into depth-estimation images:
python examples/qualcomm/oss_scripts/depthanything_v2_small.py -a $ARTIFACT -d $IMAGENET_FOLDER_PATH -b build-android/ -H $HOST_NAME -s $DEVICE_ID -m $SOC_ID --dump_example_outputResults
Example Image:

Source Model Output (with depth-estimation post-processing):

QNN-HTP Output (with depth-estimation post-processing):
