Input: - memory: bs, \sum{hw}, d_model - memory_padding_mask: bs, \sum{hw} - spatial_shapes: nlevel, 2 - learnedw

Generate attention mask between each pair of special tokens Args: input_ids (torch.Tensor): input ids. Shape: [bs, num_token] spec

Equivalent to nn.functional.interpolate, but with support for empty batch sizes. This will eventually be supported natively by PyTorch, and t

Parse information from a line in a requirements text file.

Parse the package dependencies listed in a requirements file but strips specific versioning information. Args: fname (str): path to r

This function disables printing when not in master process

Load data from json/yaml/pickle files. This method provides a unified api for loading data from serialized files. Args: file (str or

Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image

Args: max_text_len: max length of text.

Inputs: embed_dim - Dimensionality of input and attention feature vectors hidden_dim - Dimensionality of hidden layer

_summary_ Args: encoder_layer (_type_): _description_ num_layers (_type_): _description_ norm (_type_, op