mapanything/third_party/track_modules/track_refine.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.
#
# Modified from https://github.com/facebookresearch/vggt

from typing import Tuple

import torch
from einops import rearrange


def refine_track(

    images,

    fine_fnet,

    fine_tracker,

    coarse_pred,

    compute_score=False,

    pradius=15,

    sradius=2,

    fine_iters=6,

    chunk=40960,

):
    """

    Refines the tracking of images using a fine track predictor and a fine feature network.

    Check https://arxiv.org/abs/2312.04563 for more details.



    Args:

        images (torch.Tensor): The images to be tracked.

        fine_fnet (nn.Module): The fine feature network.

        fine_tracker (nn.Module): The fine track predictor.

        coarse_pred (torch.Tensor): The coarse predictions of tracks.

        compute_score (bool, optional): Whether to compute the score. Defaults to False.

        pradius (int, optional): The radius of a patch. Defaults to 15.

        sradius (int, optional): The search radius. Defaults to 2.



    Returns:

        torch.Tensor: The refined tracks.

        torch.Tensor, optional: The score.

    """

    # coarse_pred shape: BxSxNx2,
    # where B is the batch, S is the video/images length, and N is the number of tracks
    # now we are going to extract patches with the center at coarse_pred
    # Please note that the last dimension indicates x and y, and hence has a dim number of 2
    B, S, N, _ = coarse_pred.shape
    _, _, _, H, W = images.shape

    # Given the raidus of a patch, compute the patch size
    psize = pradius * 2 + 1

    # Note that we assume the first frame is the query frame
    # so the 2D locations of the first frame are the query points
    query_points = coarse_pred[:, 0]

    # Given 2D positions, we can use grid_sample to extract patches
    # but it takes too much memory.
    # Instead, we use the floored track xy to sample patches.

    # For example, if the query point xy is (128.16, 252.78),
    # and the patch size is (31, 31),
    # our goal is to extract the content of a rectangle
    # with left top: (113.16, 237.78)
    # and right bottom: (143.16, 267.78).
    # However, we record the floored left top: (113, 237)
    # and the offset (0.16, 0.78)
    # Then what we need is just unfolding the images like in CNN,
    # picking the content at [(113, 237), (143, 267)].
    # Such operations are highly optimized at pytorch
    # (well if you really want to use interpolation, check the function extract_glimpse() below)

    with torch.no_grad():
        content_to_extract = images.reshape(B * S, 3, H, W)
        C_in = content_to_extract.shape[1]

        # Please refer to https://pytorch.org/docs/stable/generated/torch.nn.Unfold.html
        # for the detailed explanation of unfold()
        # Here it runs sliding windows (psize x psize) to build patches
        # The shape changes from
        # (B*S)x C_in x H x W to (B*S)x C_in x H_new x W_new x Psize x Psize
        # where Psize is the size of patch
        content_to_extract = content_to_extract.unfold(2, psize, 1).unfold(3, psize, 1)

    # Floor the coarse predictions to get integers and save the fractional/decimal
    track_int = coarse_pred.floor().int()
    track_frac = coarse_pred - track_int

    # Note the points represent the center of patches
    # now we get the location of the top left corner of patches
    # because the ouput of pytorch unfold are indexed by top left corner
    topleft = track_int - pradius
    topleft_BSN = topleft.clone()

    # clamp the values so that we will not go out of indexes
    # NOTE: (VERY IMPORTANT: This operation ASSUMES H=W).
    # You need to seperately clamp x and y if H!=W
    topleft = topleft.clamp(0, H - psize)

    # Reshape from BxSxNx2 -> (B*S)xNx2
    topleft = topleft.reshape(B * S, N, 2)

    # Prepare batches for indexing, shape: (B*S)xN
    batch_indices = (
        torch.arange(B * S)[:, None].expand(-1, N).to(content_to_extract.device)
    )

    # extracted_patches: (B*S) x N x C_in x Psize x Psize
    extracted_patches = content_to_extract[
        batch_indices, :, topleft[..., 1], topleft[..., 0]
    ]

    if chunk < 0:
        # Extract image patches based on top left corners
        # Feed patches to fine fent for features
        patch_feat = fine_fnet(extracted_patches.reshape(B * S * N, C_in, psize, psize))
    else:
        patches = extracted_patches.reshape(B * S * N, C_in, psize, psize)

        patch_feat_list = []
        for p in torch.split(patches, chunk):
            patch_feat_list += [fine_fnet(p)]
        patch_feat = torch.cat(patch_feat_list, 0)

    C_out = patch_feat.shape[1]

    # Refine the coarse tracks by fine_tracker
    # reshape back to B x S x N x C_out x Psize x Psize
    patch_feat = patch_feat.reshape(B, S, N, C_out, psize, psize)
    patch_feat = rearrange(patch_feat, "b s n c p q -> (b n) s c p q")

    # Prepare for the query points for fine tracker
    # They are relative to the patch left top corner,
    # instead of the image top left corner now
    # patch_query_points: N x 1 x 2
    # only 1 here because for each patch we only have 1 query point
    patch_query_points = track_frac[:, 0] + pradius
    patch_query_points = patch_query_points.reshape(B * N, 2).unsqueeze(1)

    # Feed the PATCH query points and tracks into fine tracker
    fine_pred_track_lists, _, _, query_point_feat = fine_tracker(
        query_points=patch_query_points,
        fmaps=patch_feat,
        iters=fine_iters,
        return_feat=True,
    )

    # relative the patch top left
    fine_pred_track = fine_pred_track_lists[-1].clone()

    # From (relative to the patch top left) to (relative to the image top left)
    for idx in range(len(fine_pred_track_lists)):
        fine_level = rearrange(
            fine_pred_track_lists[idx], "(b n) s u v -> b s n u v", b=B, n=N
        )
        fine_level = fine_level.squeeze(-2)
        fine_level = fine_level + topleft_BSN
        fine_pred_track_lists[idx] = fine_level

    # relative to the image top left
    refined_tracks = fine_pred_track_lists[-1].clone()
    refined_tracks[:, 0] = query_points

    score = None

    if compute_score:
        score = compute_score_fn(
            query_point_feat,
            patch_feat,
            fine_pred_track,
            sradius,
            psize,
            B,
            N,
            S,
            C_out,
        )

    return refined_tracks, score


def refine_track_v0(

    images,

    fine_fnet,

    fine_tracker,

    coarse_pred,

    compute_score=False,

    pradius=15,

    sradius=2,

    fine_iters=6,

):
    """

    COPIED FROM VGGSfM



    Refines the tracking of images using a fine track predictor and a fine feature network.

    Check https://arxiv.org/abs/2312.04563 for more details.



    Args:

        images (torch.Tensor): The images to be tracked.

        fine_fnet (nn.Module): The fine feature network.

        fine_tracker (nn.Module): The fine track predictor.

        coarse_pred (torch.Tensor): The coarse predictions of tracks.

        compute_score (bool, optional): Whether to compute the score. Defaults to False.

        pradius (int, optional): The radius of a patch. Defaults to 15.

        sradius (int, optional): The search radius. Defaults to 2.



    Returns:

        torch.Tensor: The refined tracks.

        torch.Tensor, optional: The score.

    """

    # coarse_pred shape: BxSxNx2,
    # where B is the batch, S is the video/images length, and N is the number of tracks
    # now we are going to extract patches with the center at coarse_pred
    # Please note that the last dimension indicates x and y, and hence has a dim number of 2
    B, S, N, _ = coarse_pred.shape
    _, _, _, H, W = images.shape

    # Given the raidus of a patch, compute the patch size
    psize = pradius * 2 + 1

    # Note that we assume the first frame is the query frame
    # so the 2D locations of the first frame are the query points
    query_points = coarse_pred[:, 0]

    # Given 2D positions, we can use grid_sample to extract patches
    # but it takes too much memory.
    # Instead, we use the floored track xy to sample patches.

    # For example, if the query point xy is (128.16, 252.78),
    # and the patch size is (31, 31),
    # our goal is to extract the content of a rectangle
    # with left top: (113.16, 237.78)
    # and right bottom: (143.16, 267.78).
    # However, we record the floored left top: (113, 237)
    # and the offset (0.16, 0.78)
    # Then what we need is just unfolding the images like in CNN,
    # picking the content at [(113, 237), (143, 267)].
    # Such operations are highly optimized at pytorch
    # (well if you really want to use interpolation, check the function extract_glimpse() below)

    with torch.no_grad():
        content_to_extract = images.reshape(B * S, 3, H, W)
        C_in = content_to_extract.shape[1]

        # Please refer to https://pytorch.org/docs/stable/generated/torch.nn.Unfold.html
        # for the detailed explanation of unfold()
        # Here it runs sliding windows (psize x psize) to build patches
        # The shape changes from
        # (B*S)x C_in x H x W to (B*S)x C_in x H_new x W_new x Psize x Psize
        # where Psize is the size of patch
        content_to_extract = content_to_extract.unfold(2, psize, 1).unfold(3, psize, 1)

    # Floor the coarse predictions to get integers and save the fractional/decimal
    track_int = coarse_pred.floor().int()
    track_frac = coarse_pred - track_int

    # Note the points represent the center of patches
    # now we get the location of the top left corner of patches
    # because the ouput of pytorch unfold are indexed by top left corner
    topleft = track_int - pradius
    topleft_BSN = topleft.clone()

    # clamp the values so that we will not go out of indexes
    # NOTE: (VERY IMPORTANT: This operation ASSUMES H=W).
    # You need to seperately clamp x and y if H!=W
    topleft = topleft.clamp(0, H - psize)

    # Reshape from BxSxNx2 -> (B*S)xNx2
    topleft = topleft.reshape(B * S, N, 2)

    # Prepare batches for indexing, shape: (B*S)xN
    batch_indices = (
        torch.arange(B * S)[:, None].expand(-1, N).to(content_to_extract.device)
    )

    # Extract image patches based on top left corners
    # extracted_patches: (B*S) x N x C_in x Psize x Psize
    extracted_patches = content_to_extract[
        batch_indices, :, topleft[..., 1], topleft[..., 0]
    ]

    # Feed patches to fine fent for features
    patch_feat = fine_fnet(extracted_patches.reshape(B * S * N, C_in, psize, psize))

    C_out = patch_feat.shape[1]

    # Refine the coarse tracks by fine_tracker

    # reshape back to B x S x N x C_out x Psize x Psize
    patch_feat = patch_feat.reshape(B, S, N, C_out, psize, psize)
    patch_feat = rearrange(patch_feat, "b s n c p q -> (b n) s c p q")

    # Prepare for the query points for fine tracker
    # They are relative to the patch left top corner,
    # instead of the image top left corner now
    # patch_query_points: N x 1 x 2
    # only 1 here because for each patch we only have 1 query point
    patch_query_points = track_frac[:, 0] + pradius
    patch_query_points = patch_query_points.reshape(B * N, 2).unsqueeze(1)

    # Feed the PATCH query points and tracks into fine tracker
    fine_pred_track_lists, _, _, query_point_feat = fine_tracker(
        query_points=patch_query_points,
        fmaps=patch_feat,
        iters=fine_iters,
        return_feat=True,
    )

    # relative the patch top left
    fine_pred_track = fine_pred_track_lists[-1].clone()

    # From (relative to the patch top left) to (relative to the image top left)
    for idx in range(len(fine_pred_track_lists)):
        fine_level = rearrange(
            fine_pred_track_lists[idx], "(b n) s u v -> b s n u v", b=B, n=N
        )
        fine_level = fine_level.squeeze(-2)
        fine_level = fine_level + topleft_BSN
        fine_pred_track_lists[idx] = fine_level

    # relative to the image top left
    refined_tracks = fine_pred_track_lists[-1].clone()
    refined_tracks[:, 0] = query_points

    score = None

    if compute_score:
        score = compute_score_fn(
            query_point_feat,
            patch_feat,
            fine_pred_track,
            sradius,
            psize,
            B,
            N,
            S,
            C_out,
        )

    return refined_tracks, score


################################## NOTE: NOT USED ##################################


def compute_score_fn(

    query_point_feat, patch_feat, fine_pred_track, sradius, psize, B, N, S, C_out

):
    """

    Compute the scores, i.e., the standard deviation of the 2D similarity heatmaps,

    given the query point features and reference frame feature maps

    """

    from kornia.geometry.subpix import dsnt
    from kornia.utils.grid import create_meshgrid

    # query_point_feat initial shape: B x N x C_out,
    # query_point_feat indicates the feat at the coorponsing query points
    # Therefore we don't have S dimension here
    query_point_feat = query_point_feat.reshape(B, N, C_out)
    # reshape and expand to B x (S-1) x N x C_out
    query_point_feat = query_point_feat.unsqueeze(1).expand(-1, S - 1, -1, -1)
    # and reshape to (B*(S-1)*N) x C_out
    query_point_feat = query_point_feat.reshape(B * (S - 1) * N, C_out)

    # Radius and size for computing the score
    ssize = sradius * 2 + 1

    # Reshape, you know it, so many reshaping operations
    patch_feat = rearrange(patch_feat, "(b n) s c p q -> b s n c p q", b=B, n=N)

    # Again, we unfold the patches to smaller patches
    # so that we can then focus on smaller patches
    # patch_feat_unfold shape:
    # B x S x N x C_out x (psize - 2*sradius) x (psize - 2*sradius) x ssize x ssize
    # well a bit scary, but actually not
    patch_feat_unfold = patch_feat.unfold(4, ssize, 1).unfold(5, ssize, 1)

    # Do the same stuffs above, i.e., the same as extracting patches
    fine_prediction_floor = fine_pred_track.floor().int()
    fine_level_floor_topleft = fine_prediction_floor - sradius

    # Clamp to ensure the smaller patch is valid
    fine_level_floor_topleft = fine_level_floor_topleft.clamp(0, psize - ssize)
    fine_level_floor_topleft = fine_level_floor_topleft.squeeze(2)

    # Prepare the batch indices and xy locations

    batch_indices_score = torch.arange(B)[:, None, None].expand(-1, S, N)  # BxSxN
    batch_indices_score = batch_indices_score.reshape(-1).to(
        patch_feat_unfold.device
    )  # B*S*N
    y_indices = fine_level_floor_topleft[..., 0].flatten()  # Flatten H indices
    x_indices = fine_level_floor_topleft[..., 1].flatten()  # Flatten W indices

    reference_frame_feat = patch_feat_unfold.reshape(
        B * S * N, C_out, psize - sradius * 2, psize - sradius * 2, ssize, ssize
    )

    # Note again, according to pytorch convention
    # x_indices cooresponds to [..., 1] and y_indices cooresponds to [..., 0]
    reference_frame_feat = reference_frame_feat[
        batch_indices_score, :, x_indices, y_indices
    ]
    reference_frame_feat = reference_frame_feat.reshape(B, S, N, C_out, ssize, ssize)
    # pick the frames other than the first one, so we have S-1 frames here
    reference_frame_feat = reference_frame_feat[:, 1:].reshape(
        B * (S - 1) * N, C_out, ssize * ssize
    )

    # Compute similarity
    sim_matrix = torch.einsum("mc,mcr->mr", query_point_feat, reference_frame_feat)
    softmax_temp = 1.0 / C_out**0.5
    heatmap = torch.softmax(softmax_temp * sim_matrix, dim=1)
    # 2D heatmaps
    heatmap = heatmap.reshape(B * (S - 1) * N, ssize, ssize)  # * x ssize x ssize

    coords_normalized = dsnt.spatial_expectation2d(heatmap[None], True)[0]
    grid_normalized = create_meshgrid(
        ssize, ssize, normalized_coordinates=True, device=heatmap.device
    ).reshape(1, -1, 2)

    var = (
        torch.sum(grid_normalized**2 * heatmap.view(-1, ssize * ssize, 1), dim=1)
        - coords_normalized**2
    )
    std = torch.sum(
        torch.sqrt(torch.clamp(var, min=1e-10)), -1
    )  # clamp needed for numerical stability

    score = std.reshape(B, S - 1, N)
    # set score as 1 for the query frame
    score = torch.cat([torch.ones_like(score[:, 0:1]), score], dim=1)

    return score


def extract_glimpse(

    tensor: torch.Tensor,

    size: Tuple[int, int],

    offsets,

    mode="bilinear",

    padding_mode="zeros",

    debug=False,

    orib=None,

):
    B, C, W, H = tensor.shape

    h, w = size
    xs = torch.arange(0, w, dtype=tensor.dtype, device=tensor.device) - (w - 1) / 2.0
    ys = torch.arange(0, h, dtype=tensor.dtype, device=tensor.device) - (h - 1) / 2.0

    vy, vx = torch.meshgrid(ys, xs)
    grid = torch.stack([vx, vy], dim=-1)  # h, w, 2
    grid = grid[None]

    B, N, _ = offsets.shape

    offsets = offsets.reshape((B * N), 1, 1, 2)
    offsets_grid = offsets + grid

    # normalised grid  to [-1, 1]
    offsets_grid = (
        offsets_grid - offsets_grid.new_tensor([W / 2, H / 2])
    ) / offsets_grid.new_tensor([W / 2, H / 2])

    # BxCxHxW -> Bx1xCxHxW
    tensor = tensor[:, None]

    # Bx1xCxHxW -> BxNxCxHxW
    tensor = tensor.expand(-1, N, -1, -1, -1)

    # BxNxCxHxW -> (B*N)xCxHxW
    tensor = tensor.reshape((B * N), C, W, H)

    sampled = torch.nn.functional.grid_sample(
        tensor, offsets_grid, mode=mode, align_corners=False, padding_mode=padding_mode
    )

    # NOTE: I am not sure it should be h, w or w, h here
    # but okay for sqaures
    sampled = sampled.reshape(B, N, C, h, w)

    return sampled