cotracker/build/lib/models/core/embeddings.py

# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.

# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

from typing import Tuple, Union
import torch


def get_2d_sincos_pos_embed(
    embed_dim: int, grid_size: Union[int, Tuple[int, int]]
) -> torch.Tensor:
    """
    This function initializes a grid and generates a 2D positional embedding using sine and cosine functions.
    It is a wrapper of get_2d_sincos_pos_embed_from_grid.
    Args:
    - embed_dim: The embedding dimension.
    - grid_size: The grid size.
    Returns:
    - pos_embed: The generated 2D positional embedding.
    """
    if isinstance(grid_size, tuple):
        grid_size_h, grid_size_w = grid_size
    else:
        grid_size_h = grid_size_w = grid_size
    grid_h = torch.arange(grid_size_h, dtype=torch.float)
    grid_w = torch.arange(grid_size_w, dtype=torch.float)
    grid = torch.meshgrid(grid_w, grid_h, indexing="xy")
    grid = torch.stack(grid, dim=0)
    grid = grid.reshape([2, 1, grid_size_h, grid_size_w])
    pos_embed = get_2d_sincos_pos_embed_from_grid(embed_dim, grid)
    return pos_embed.reshape(1, grid_size_h, grid_size_w, -1).permute(0, 3, 1, 2)


def get_2d_sincos_pos_embed_from_grid(
    embed_dim: int, grid: torch.Tensor
) -> torch.Tensor:
    """
    This function generates a 2D positional embedding from a given grid using sine and cosine functions.

    Args:
    - embed_dim: The embedding dimension.
    - grid: The grid to generate the embedding from.

    Returns:
    - emb: The generated 2D positional embedding.
    """
    assert embed_dim % 2 == 0

    # use half of dimensions to encode grid_h
    emb_h = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[0])  # (H*W, D/2)
    emb_w = get_1d_sincos_pos_embed_from_grid(embed_dim // 2, grid[1])  # (H*W, D/2)

    emb = torch.cat([emb_h, emb_w], dim=2)  # (H*W, D)
    return emb


def get_1d_sincos_pos_embed_from_grid(
    embed_dim: int, pos: torch.Tensor
) -> torch.Tensor:
    """
    This function generates a 1D positional embedding from a given grid using sine and cosine functions.

    Args:
    - embed_dim: The embedding dimension.
    - pos: The position to generate the embedding from.

    Returns:
    - emb: The generated 1D positional embedding.
    """
    assert embed_dim % 2 == 0
    omega = torch.arange(embed_dim // 2, dtype=torch.double)
    omega /= embed_dim / 2.0
    omega = 1.0 / 10000**omega  # (D/2,)

    pos = pos.reshape(-1)  # (M,)
    out = torch.einsum("m,d->md", pos, omega)  # (M, D/2), outer product

    emb_sin = torch.sin(out)  # (M, D/2)
    emb_cos = torch.cos(out)  # (M, D/2)

    emb = torch.cat([emb_sin, emb_cos], dim=1)  # (M, D)
    return emb[None].float()


def get_2d_embedding(xy: torch.Tensor, C: int, cat_coords: bool = True) -> torch.Tensor:
    """
    This function generates a 2D positional embedding from given coordinates using sine and cosine functions.

    Args:
    - xy: The coordinates to generate the embedding from.
    - C: The size of the embedding.
    - cat_coords: A flag to indicate whether to concatenate the original coordinates to the embedding.

    Returns:
    - pe: The generated 2D positional embedding.
    """
    B, N, D = xy.shape
    assert D == 2

    x = xy[:, :, 0:1]
    y = xy[:, :, 1:2]
    div_term = (
        torch.arange(0, C, 2, device=xy.device, dtype=torch.float32) * (1000.0 / C)
    ).reshape(1, 1, int(C / 2))

    pe_x = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32)
    pe_y = torch.zeros(B, N, C, device=xy.device, dtype=torch.float32)

    pe_x[:, :, 0::2] = torch.sin(x * div_term)
    pe_x[:, :, 1::2] = torch.cos(x * div_term)

    pe_y[:, :, 0::2] = torch.sin(y * div_term)
    pe_y[:, :, 1::2] = torch.cos(y * div_term)

    pe = torch.cat([pe_x, pe_y], dim=2)  # (B, N, C*3)
    if cat_coords:
        pe = torch.cat([xy, pe], dim=2)  # (B, N, C*3+3)
    return pe