modosaic.segmentation.generators.impl.sam_3

modosaic.segmentation.generators.impl.sam_3

SAM3

SAM3()

Bases: SegmentationGenerator

SAM 3 mask-generation pipeline.

Load the pinned SAM 3 Transformers pipeline.

Source code in modosaic/segmentation/generators/impl/sam_3.py

def __init__(self):
    """Load the pinned SAM 3 Transformers pipeline."""
    super().__init__()
    self.model_specs = HFModelSpec(
        model_id="facebook/sam3",
        revision="3c879f39826c281e95690f02c7821c4de09afae7",
        # The Transformers SAM mask postprocessor runs Torchvision NMS with
        # float32 boxes, so scores must stay float32 as well.
        dtype=torch.float32,
    )
    self.pipeline = self._load_pipeline()

generate

generate(record)

Generate segmentation masks for one image record.

Source code in modosaic/segmentation/generators/impl/sam_3.py

@override
def generate(self, record: ImageRecord) -> list[np.ndarray]:
    """Generate segmentation masks for one image record."""
    logger.debug(f"Generating segmentation masks for record sample {record.sample_id}")
    img = ImageService.bytes_to_pil(record.image_bytes)
    masks = self.pipeline(img, points_per_batch=64)["masks"]
    logger.debug(f"Generated {len(masks)} segmentation masks for record sample {record.sample_id}")
    return [
        mask.detach().cpu().float().numpy()
        for mask in masks
    ]