Generate positional encoding for a grid of the specified size.

Predict masks given image and prompt embeddings. Arguments: image_embeddings (torch.Tensor): the embeddings from the image

Args: image_embedding (torch.Tensor): image to attend to. Should be shape B x embedding_dim x h x w for any h and w.

Embeds different types of prompts, returning both sparse and dense embeddings. Arguments: points (tuple(torch.Tens

Generates masks for the given image. Arguments: image (np.ndarray): The image to generate masks for, in HWC uint8 format.

Returns the image embeddings for the currently set image, with shape 1xCxHxW, where C is the embedding dimension and (H,W) are

return given network

Initialize a inference state.

compute bounding box given an input mask Inputs: - masks: [B, 1, H, W] boxes, dtype=torch.Tensor Returns: - box_coords: [B, 1,

Removes small disconnected regions and holes in masks, then reruns box NMS to remove any new duplicates. Edits mask_data in

Predict masks for the given input prompts, using the currently set image. Arguments: point_coords (np.ndarray or None): A

This function is very similar to predict(...), however it is used for batched mode, when the model is expected to generate predictions on multiple ima

Save a given Tensor into an image file. Args: tensor (Tensor or list): Image to be saved. If given a mini-batch tensor, s

Calculates the image embeddings for the provided image batch, allowing masks to be predicted with the 'predict_batch' method.