Using a SAM 2 model, generates masks for the entire image. Generates a grid of point prompts over the image, then filters low

Uses SAM-2 to calculate the image embedding for an image, and then allow repeated, efficient mask prediction given prompts.

Args: kernel_size (Tuple): kernel size of the projection layer. stride (Tuple): stride of the projection layer.

Predicts masks given an image and prompt embeddings, using a transformer architecture. Arguments: transformer_dim

A transformer decoder that attends to an input image using queries whose positional embedding is supplied. Args: d

A transformer block with four layers: (1) self-attention of sparse inputs, (2) cross attention of sparse inputs to dense inputs, (3)

Encodes prompts for input to SAM's mask decoder. Arguments: embed_dim (int): The prompts' embedding dimension im

Transforms for SAM2.

Args: cfg (CfgNode): model (SemanticSegmentor): a SemanticSegmentor to apply TTA on. tta_mapper (callable

Multi-Scale Deformable Attention Module :param d_model hidden dimension :param n_levels number of feature levels

Creates the matcher Params: cost_class: This is the relative weight of the classification error in the matching cost

Create the criterion. Parameters: num_classes: number of object categories, omitting the special no-object category ma