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switch to a new implementation of the class SAM2VideoPredictor for per-object inference sam2/sam2_video_predictor.py
In this PR, we switch to a new implementation of the class `SAM2VideoPredictor` for in sam2/sam2_video_predictor.py, which allows for independent per-object inference. Specifically, the new `SAM2VideoPredictor`: * it handles the inference of each object separately, as if we are opening a separate session for each object * it relaxes the assumption on prompting * previously if a frame receives clicks only for a subset of objects, the rest of (non-prompted) objects are assumed to be non-existing in this frame * now if a frame receives clicks only for a subset of objects, we don't make any assumptions for the remaining (non-prompted) objects * it allows adding new objects after tracking starts * (The previous implementation is backed up to `SAM2VideoPredictor` in sam2/sam2_video_predictor_legacy.py) Also, fix a small typo `APP_URL` => `API_URL` in the doc. Test plan: tested with the predictor notebook `notebooks/video_predictor_example.ipynb` and VOS script `tools/vos_inference.py`. Also tested with the demo.
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@@ -105,7 +105,7 @@ cd demo/backend/server/
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```bash
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PYTORCH_ENABLE_MPS_FALLBACK=1 \
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APP_ROOT="$(pwd)/../../../" \
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APP_URL=http://localhost:7263 \
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API_URL=http://localhost:7263 \
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MODEL_SIZE=base_plus \
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DATA_PATH="$(pwd)/../../data" \
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DEFAULT_VIDEO_PATH=gallery/05_default_juggle.mp4 \
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