Whether the input is an string instance. Note: This method is deprecated since python 2 is no longer supported.

Return an activation function given a string

str: a "nice" summary string describing this module

merge dict `a` into dict `b` (non-inplace). values in `a` will overwrite `b`. copy first to avoid inplace modification

Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]

Return: captions: str cat2tokenspan: dict { 'dog': [[0, 2]], ... }

construct a map such that positive_map[i,j] = True iff box i is associated to token j Input: - tokenized: - input_ids: Tensor[

Forward function.

Args: x (float): Input images(BxCxHxW) axis (int): The index for weighted mean other_axis (int): The other index Re

generate sine position embedding from a position tensor Args: pos_tensor (torch.Tensor): shape: [..., n]. num_pos_feats (int): pro

Default initialization for Parameters of Module.

L2-normalize columns of X

Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C)

str: the string of the module

Return a process group based on gloo backend, containing all the ranks The result is cached.

Run all_gather on arbitrary picklable data (not necessarily tensors) Args: data: any picklable object Returns: list[data]

This function annotates an image with bounding boxes and labels. Parameters: image_source (np.ndarray): The source image to be annot

Useful args: - backbone: backbone name - lr_backbone: - dilation - return_interm_indices: available: [0,1,2,3], [

Coerces input into a random number generator. If the input is None, then a global random state is returned. If the input is a numeric value,

Run per image evaluation on given images and store results (a list of dict) in self.evalImgs :return: None