Transform so that the first camera is the origin

make sure the poses in on window are normalized by the first frame, where the first frame transformation is the Identity Matrix. NOTE: P

Flattens a nested dictionary into a single-level dictionary, with keys as tuples.

Applies the flow color wheel to (possibly clipped) flow components u and v. According to the C++ source code of Daniel Scharstein Accord

forward the video camera model, which predict ( `intr` `camera poses` `video depth` ) args: x: t

x_list contains a list of tensors to nest together and run

This function generates a 1D positional embedding from a given grid using sine and cosine functions. Args: - embed_dim: The embedding di

This function generates a 2D positional embedding from given coordinates using sine and cosine functions. Args: - xy: The coordinates to

This function initializes a grid and generates a 2D positional embedding using sine and cosine functions. It is a wrapper of get_2d_sincos_po

This function generates a 2D positional embedding from a given grid using sine and cosine functions. Args: - embed_dim: The embedding di

Creates 2D sinusoidal positional embeddings. Args: embed_dim (`int`): The embedding dimension. grid_size (`int`)

r""" Creates 3D sinusoidal positional embeddings. Args: embed_dim (`int`): The embedding dimension of inputs. It must be

Args: embed_dim: int box: a 3-D tensor [B x N x 4] representing the bounding boxes for GLIGEN pipeline Returns: [B x

r""" Get the attention processor in use. Args: return_deprecated_lora (`bool`, *optional*, defaults to `False`):

This matches the implementation in Denoising Diffusion Probabilistic Models: Create sinusoidal timestep embeddings. Args timesteps (

Given all tracks in one sample (b), compute a (N,3) array of RGB color values in [0,255]. The color is determined by the (x,y) position in th

A context where the model is temporarily changed to eval mode, and restored to previous mode afterwards. Args: model: a torch Mo

Dump an "Instances" object to a COCO-format json that's used for evaluation. Args: instances (Instances): img_id (int): the

Dump an "Instances" object to a COCO-format json that's used for evaluation. Args: instances (Instances): img_id (int): the

x: (B, T, N, 2) return: indices of k-NN, shape (B, T, N, k)

Args: image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". gt_dir (str): path to the raw annotations. e.g., "~/coc

Args: image_dir (str): path to the raw dataset. e.g., "~/cityscapes/leftImg8bit/train". gt_dir (str): path to the raw annotations

Args: image_dir (str): path to the raw dataset. e.g., "~/coco/train2017". gt_dir (str): path to the raw annotations. e.g., "~/coc