SAM 2 Update 12/11/2024 -- full model compilation for a major VOS speedup and a new SAM2VideoPredictor to better handle multi-object tracking (#486)

This PR provides new features and updates for SAM 2:

- We now support `torch.compile` of the entire SAM 2 model on videos, which can be turned on by setting `vos_optimized=True` in `build_sam2_video_predictor` (it uses the new `SAM2VideoPredictorVOS` predictor class in `sam2/sam2_video_predictor.py`).
  * Compared to the previous setting (which only compiles the image encoder backbone), the new full model compilation gives a major speedup in inference FPS.
  * In the VOS prediction script `tools/vos_inference.py`, you can specify this option in `tools/vos_inference.py` via the `--use_vos_optimized_video_predictor` flag.
  * Note that turning on this flag might introduce a small variance in the predictions due to numerical differences caused by `torch.compile` of the full model.
  * **PyTorch 2.5.1 is the minimum version for full support of this feature**. (Earlier PyTorch versions might run into compilation errors in some cases.) Therefore, we have updated the minimum PyTorch version to 2.5.1 accordingly in the installation scripts.
- We also update the implementation of the `SAM2VideoPredictor` class for the SAM 2 video prediction in `sam2/sam2_video_predictor.py`, which allows for independent per-object inference. Specifically, in the new `SAM2VideoPredictor`:
  * Now **we handle the inference of each object independently** (as if we are opening a separate session for each object) while sharing their backbone features.
  * This change allows us to relax the assumption of prompting for multi-object tracking. Previously (due to the batching behavior in inference), if a video frame receives clicks for only a subset of objects, the rest of the (non-prompted) objects are assumed to be non-existent in this frame (i.e., in such frames, the user is telling SAM 2 that the rest of the objects don't appear). Now, if a frame receives clicks for only a subset of objects, we do not make any assumptions about the remaining (non-prompted) objects (i.e., now each object is handled independently and is not affected by how other objects are prompted). As a result, **we allow adding new objects after tracking starts** after this change (which was previously a restriction on usage).
  * We believe that the new version is a more natural inference behavior and therefore switched to it as the default behavior. The previous implementation of `SAM2VideoPredictor` is backed up to in `sam2/sam2_video_predictor_legacy.py`. All the VOS inference results using `tools/vos_inference.py` should remain the same after this change to the `SAM2VideoPredictor` class.

sam2/build_sam.py

@@ -104,11 +104,18 @@ def build_sam2_video_predictor(
     mode="eval",
     hydra_overrides_extra=[],
     apply_postprocessing=True,
+    vos_optimized=False,
     **kwargs,
 ):
     hydra_overrides = [
         "++model._target_=sam2.sam2_video_predictor.SAM2VideoPredictor",
     ]
+    if vos_optimized:
+        hydra_overrides = [
+            "++model._target_=sam2.sam2_video_predictor.SAM2VideoPredictorVOS",
+            "++model.compile_image_encoder=True",  # Let sam2_base handle this
+        ]
+
     if apply_postprocessing:
         hydra_overrides_extra = hydra_overrides_extra.copy()
         hydra_overrides_extra += [