If the user has "sam2/sam2" in their path, they are likey importing the repo itself as "sam2" rather than importing the "sam2" python package (i.e. "sam2/sam2" directory). This typically happens because the user is running Python from the parent directory that contains the sam2 repo they cloned.
In general, the user should not run Python from the parent dir when the repo is cloned into (same is true for e.g. Numpy repo that contains names like `numpy/numpy` where the module and the repo have the same name), as the user encountered in https://github.com/facebookresearch/sam2/issues/346.
(close https://github.com/facebookresearch/sam2/issues/346)
This PR update repo links after we renamed the repo from `segment-anything-2` to `sam2`. It also changes `NAME` in setup.py to `SAM-2` (which is already the named used in pip setup since python packages don't allow whitespace)
This PR adds an example to provide box prompt in SAM 2 as inputs to the `add_new_points_or_box` API (renamed from`add_new_points`, which is kept for backward compatibility). If `box` is provided, we add it as the first two points with labels 2 and 3, along with the user-provided points (consistent with how SAM 2 is trained).
The video predictor notebook `notebooks/video_predictor_example.ipynb` is updated to include segmenting from box prompt as an example.
In this PR, we make it optional to build the SAM 2 CUDA extension, in observation that many users encounter difficulties with the CUDA compilation step.
1. During installation, we catch build errors and print a warning message. We also allow explicitly turning off the CUDA extension building with `SAM2_BUILD_CUDA=0`.
2. At runtime, we catch CUDA kernel errors from connected components and print a warning on skipping the post processing step.
We also fall back to the all available kernels if the Flash Attention kernel fails.
sam2/build_sam.py:81:14: TOR102 [*] `torch.load` without `weights_only` parameter is unsafe. Explicitly set `weights_only` to False only if you trust the data you load and full pickle functionality is needed, otherwise set `weights_only=True`.
Found with https://github.com/pytorch-labs/torchfix/
