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# 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
import logging
import warnings
import torch
import torch.nn.functional as F
from lightglue import ALIKED, SIFT, SuperPoint
from .vggsfm_tracker import TrackerPredictor
# Suppress verbose logging from dependencies
logging.getLogger("dinov2").setLevel(logging.WARNING)
warnings.filterwarnings("ignore", message="xFormers is available")
warnings.filterwarnings("ignore", message="dinov2")
# Constants
_RESNET_MEAN = [0.485, 0.456, 0.406]
_RESNET_STD = [0.229, 0.224, 0.225]
def build_vggsfm_tracker(model_path=None):
"""
Build and initialize the VGGSfM tracker.
Args:
model_path: Path to the model weights file. If None, weights are downloaded from HuggingFace.
Returns:
Initialized tracker model in eval mode.
"""
tracker = TrackerPredictor()
if model_path is None:
default_url = (
"https://huggingface.co/facebook/VGGSfM/resolve/main/vggsfm_v2_tracker.pt"
)
tracker.load_state_dict(torch.hub.load_state_dict_from_url(default_url))
else:
tracker.load_state_dict(torch.load(model_path))
tracker.eval()
return tracker
def generate_rank_by_dino(
images,
query_frame_num,
image_size=336,
model_name="dinov2_vitb14_reg",
device="cuda",
spatial_similarity=False,
):
"""
Generate a ranking of frames using DINO ViT features.
Args:
images: Tensor of shape (S, 3, H, W) with values in range [0, 1]
query_frame_num: Number of frames to select
image_size: Size to resize images to before processing
model_name: Name of the DINO model to use
device: Device to run the model on
spatial_similarity: Whether to use spatial token similarity or CLS token similarity
Returns:
List of frame indices ranked by their representativeness
"""
# Resize images to the target size
images = F.interpolate(
images, (image_size, image_size), mode="bilinear", align_corners=False
)
# Load DINO model
dino_v2_model = torch.hub.load("facebookresearch/dinov2", model_name)
dino_v2_model.eval()
dino_v2_model = dino_v2_model.to(device)
# Normalize images using ResNet normalization
resnet_mean = torch.tensor(_RESNET_MEAN, device=device).view(1, 3, 1, 1)
resnet_std = torch.tensor(_RESNET_STD, device=device).view(1, 3, 1, 1)
images_resnet_norm = (images - resnet_mean) / resnet_std
with torch.no_grad():
frame_feat = dino_v2_model(images_resnet_norm, is_training=True)
# Process features based on similarity type
if spatial_similarity:
frame_feat = frame_feat["x_norm_patchtokens"]
frame_feat_norm = F.normalize(frame_feat, p=2, dim=1)
# Compute the similarity matrix
frame_feat_norm = frame_feat_norm.permute(1, 0, 2)
similarity_matrix = torch.bmm(
frame_feat_norm, frame_feat_norm.transpose(-1, -2)
)
similarity_matrix = similarity_matrix.mean(dim=0)
else:
frame_feat = frame_feat["x_norm_clstoken"]
frame_feat_norm = F.normalize(frame_feat, p=2, dim=1)
similarity_matrix = torch.mm(frame_feat_norm, frame_feat_norm.transpose(-1, -2))
distance_matrix = 100 - similarity_matrix.clone()
# Ignore self-pairing
similarity_matrix.fill_diagonal_(-100)
similarity_sum = similarity_matrix.sum(dim=1)
# Find the most common frame
most_common_frame_index = torch.argmax(similarity_sum).item()
# Conduct FPS sampling starting from the most common frame
fps_idx = farthest_point_sampling(
distance_matrix, query_frame_num, most_common_frame_index
)
# Clean up all tensors and models to free memory
del frame_feat, frame_feat_norm, similarity_matrix, distance_matrix
del dino_v2_model
torch.cuda.empty_cache()
return fps_idx
def farthest_point_sampling(distance_matrix, num_samples, most_common_frame_index=0):
"""
Farthest point sampling algorithm to select diverse frames.
Args:
distance_matrix: Matrix of distances between frames
num_samples: Number of frames to select
most_common_frame_index: Index of the first frame to select
Returns:
List of selected frame indices
"""
distance_matrix = distance_matrix.clamp(min=0)
N = distance_matrix.size(0)
# Initialize with the most common frame
selected_indices = [most_common_frame_index]
check_distances = distance_matrix[selected_indices]
while len(selected_indices) < num_samples:
# Find the farthest point from the current set of selected points
farthest_point = torch.argmax(check_distances)
selected_indices.append(farthest_point.item())
check_distances = distance_matrix[farthest_point]
# Mark already selected points to avoid selecting them again
check_distances[selected_indices] = 0
# Break if all points have been selected
if len(selected_indices) == N:
break
return selected_indices
def calculate_index_mappings(query_index, S, device=None):
"""
Construct an order that switches [query_index] and [0]
so that the content of query_index would be placed at [0].
Args:
query_index: Index to swap with 0
S: Total number of elements
device: Device to place the tensor on
Returns:
Tensor of indices with the swapped order
"""
new_order = torch.arange(S)
new_order[0] = query_index
new_order[query_index] = 0
if device is not None:
new_order = new_order.to(device)
return new_order
def switch_tensor_order(tensors, order, dim=1):
"""
Reorder tensors along a specific dimension according to the given order.
Args:
tensors: List of tensors to reorder
order: Tensor of indices specifying the new order
dim: Dimension along which to reorder
Returns:
List of reordered tensors
"""
return [
torch.index_select(tensor, dim, order) if tensor is not None else None
for tensor in tensors
]
def initialize_feature_extractors(
max_query_num, det_thres=0.005, extractor_method="aliked", device="cuda"
):
"""
Initialize feature extractors that can be reused based on a method string.
Args:
max_query_num: Maximum number of keypoints to extract
det_thres: Detection threshold for keypoint extraction
extractor_method: String specifying which extractors to use (e.g., "aliked", "sp+sift", "aliked+sp+sift")
device: Device to run extraction on
Returns:
Dictionary of initialized extractors
"""
extractors = {}
methods = extractor_method.lower().split("+")
for method in methods:
method = method.strip()
if method == "aliked":
aliked_extractor = ALIKED(
max_num_keypoints=max_query_num, detection_threshold=det_thres
)
extractors["aliked"] = aliked_extractor.to(device).eval()
elif method == "sp":
sp_extractor = SuperPoint(
max_num_keypoints=max_query_num, detection_threshold=det_thres
)
extractors["sp"] = sp_extractor.to(device).eval()
elif method == "sift":
sift_extractor = SIFT(max_num_keypoints=max_query_num)
extractors["sift"] = sift_extractor.to(device).eval()
else:
print(f"Warning: Unknown feature extractor '{method}', ignoring.")
if not extractors:
print(
f"Warning: No valid extractors found in '{extractor_method}'. Using ALIKED by default."
)
aliked_extractor = ALIKED(
max_num_keypoints=max_query_num, detection_threshold=det_thres
)
extractors["aliked"] = aliked_extractor.to(device).eval()
return extractors
def extract_keypoints(query_image, extractors, round_keypoints=True):
"""
Extract keypoints using pre-initialized feature extractors.
Args:
query_image: Input image tensor (3xHxW, range [0, 1])
extractors: Dictionary of initialized extractors
Returns:
Tensor of keypoint coordinates (1xNx2)
"""
query_points = None
with torch.no_grad():
for extractor_name, extractor in extractors.items():
query_points_data = extractor.extract(query_image, invalid_mask=None)
extractor_points = query_points_data["keypoints"]
if round_keypoints:
extractor_points = extractor_points.round()
if query_points is not None:
query_points = torch.cat([query_points, extractor_points], dim=1)
else:
query_points = extractor_points
return query_points
def predict_tracks_in_chunks(
track_predictor,
images_feed,
query_points_list,
fmaps_feed,
fine_tracking,
num_splits=None,
fine_chunk=40960,
):
"""
Process a list of query points to avoid memory issues.
Args:
track_predictor (object): The track predictor object used for predicting tracks.
images_feed (torch.Tensor): A tensor of shape (B, T, C, H, W) representing a batch of images.
query_points_list (list or tuple): A list/tuple of tensors, each of shape (B, Ni, 2) representing chunks of query points.
fmaps_feed (torch.Tensor): A tensor of feature maps for the tracker.
fine_tracking (bool): Whether to perform fine tracking.
num_splits (int, optional): Ignored when query_points_list is provided. Kept for backward compatibility.
Returns:
tuple: A tuple containing the concatenated predicted tracks, visibility, and scores.
"""
# If query_points_list is not a list or tuple but a single tensor, handle it like the old version for backward compatibility
if not isinstance(query_points_list, (list, tuple)):
query_points = query_points_list
if num_splits is None:
num_splits = 1
query_points_list = torch.chunk(query_points, num_splits, dim=1)
# Ensure query_points_list is a list for iteration (as torch.chunk returns a tuple)
if isinstance(query_points_list, tuple):
query_points_list = list(query_points_list)
fine_pred_track_list = []
pred_vis_list = []
pred_score_list = []
for split_points in query_points_list:
# Feed into track predictor for each split
fine_pred_track, _, pred_vis, pred_score = track_predictor(
images_feed,
split_points,
fmaps=fmaps_feed,
fine_tracking=fine_tracking,
fine_chunk=fine_chunk,
)
fine_pred_track_list.append(fine_pred_track)
pred_vis_list.append(pred_vis)
pred_score_list.append(pred_score)
# Concatenate the results from all splits
fine_pred_track = torch.cat(fine_pred_track_list, dim=2)
pred_vis = torch.cat(pred_vis_list, dim=2)
if pred_score is not None:
pred_score = torch.cat(pred_score_list, dim=2)
else:
pred_score = None
return fine_pred_track, pred_vis, pred_score
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