reads all prediction files (nifti) in the input folder, converts the labels back to BraTS convention and saves the

Calculate the Dice coefficients for all predicted labels compared to their corresponding true labels. Args: all_predictions_mean (li

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

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.

Predicts masks end-to-end from provided images and prompts. If prompts are not known in advance, using SamPredictor is rec

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

Generate positional encoding for a grid of the specified size.

r""" encoder_hidden_states (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Sequence of h

r""" Returns:

r""" Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformer

r""" labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression lo

Args: img (PIL Image or Tensor): Image to be cropped. Returns: PIL Image or Tensor: Cropped image.