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"""
Author: Siyuan Li
Licensed: Apache-2.0 License
"""
from typing import List, Tuple
import torch
import torch.nn.functional as F
from mmdet.models.trackers.base_tracker import BaseTracker
from mmdet.registry import MODELS
from mmdet.structures import TrackDataSample
from mmdet.structures.bbox import bbox_overlaps
from mmengine.structures import InstanceData
from torch import Tensor
@MODELS.register_module()
class MasaBDDTracker(BaseTracker):
"""Tracker for MASA on BDD benchmark.
Args:
init_score_thr (float): The cls_score threshold to
initialize a new tracklet. Defaults to 0.8.
obj_score_thr (float): The cls_score threshold to
update a tracked tracklet. Defaults to 0.5.
match_score_thr (float): The match threshold. Defaults to 0.5.
memo_tracklet_frames (int): The most frames in a tracklet memory.
Defaults to 10.
memo_backdrop_frames (int): The most frames in the backdrops.
Defaults to 1.
memo_momentum (float): The momentum value for embeds updating.
Defaults to 0.8.
nms_conf_thr (float): The NMS threshold for confidence.
Defaults to 0.5.
nms_backdrop_iou_thr (float): The NMS threshold for backdrop IoU.
Defaults to 0.3.
nms_class_iou_thr (float): The NMS threshold for class IoU.
Defaults to 0.7.
with_cats (bool): Whether to track with the same category.
Defaults to False.
match_metric (str): The match metric. Can be 'bisoftmax', 'softmax', or 'cosine'. Defaults to 'bisoftmax'.
"""
def __init__(
self,
init_score_thr: float = 0.8,
obj_score_thr: float = 0.5,
match_score_thr: float = 0.5,
memo_tracklet_frames: int = 10,
memo_backdrop_frames: int = 1,
memo_momentum: float = 0.8,
nms_conf_thr: float = 0.5,
nms_backdrop_iou_thr: float = 0.3,
nms_class_iou_thr: float = 0.7,
with_cats: bool = False,
match_metric: str = "bisoftmax",
**kwargs
):
super().__init__(**kwargs)
assert 0 <= memo_momentum <= 1.0
assert memo_tracklet_frames >= 0
assert memo_backdrop_frames >= 0
self.init_score_thr = init_score_thr
self.obj_score_thr = obj_score_thr
self.match_score_thr = match_score_thr
self.memo_tracklet_frames = memo_tracklet_frames
self.memo_backdrop_frames = memo_backdrop_frames
self.memo_momentum = memo_momentum
self.nms_conf_thr = nms_conf_thr
self.nms_backdrop_iou_thr = nms_backdrop_iou_thr
self.nms_class_iou_thr = nms_class_iou_thr
self.with_cats = with_cats
assert match_metric in ["bisoftmax", "softmax", "cosine"]
self.match_metric = match_metric
self.num_tracks = 0
self.tracks = dict()
self.backdrops = []
def reset(self):
"""Reset the buffer of the tracker."""
self.num_tracks = 0
self.tracks = dict()
self.backdrops = []
def update(
self,
ids: Tensor,
bboxes: Tensor,
embeds: Tensor,
labels: Tensor,
scores: Tensor,
frame_id: int,
) -> None:
"""Tracking forward function.
Args:
ids (Tensor): of shape(N, ).
bboxes (Tensor): of shape (N, 5).
embeds (Tensor): of shape (N, 256).
labels (Tensor): of shape (N, ).
scores (Tensor): of shape (N, ).
frame_id (int): The id of current frame, 0-index.
"""
tracklet_inds = ids > -1
for id, bbox, embed, label, score in zip(
ids[tracklet_inds],
bboxes[tracklet_inds],
embeds[tracklet_inds],
labels[tracklet_inds],
scores[tracklet_inds],
):
id = int(id)
# update the tracked ones and initialize new tracks
if id in self.tracks.keys():
velocity = (bbox - self.tracks[id]["bbox"]) / (
frame_id - self.tracks[id]["last_frame"]
)
self.tracks[id]["bbox"] = bbox
self.tracks[id]["embed"] = (1 - self.memo_momentum) * self.tracks[id][
"embed"
] + self.memo_momentum * embed
self.tracks[id]["last_frame"] = frame_id
self.tracks[id]["label"] = label
self.tracks[id]["score"] = score
self.tracks[id]["velocity"] = (
self.tracks[id]["velocity"] * self.tracks[id]["acc_frame"]
+ velocity
) / (self.tracks[id]["acc_frame"] + 1)
self.tracks[id]["acc_frame"] += 1
else:
self.tracks[id] = dict(
bbox=bbox,
embed=embed,
label=label,
score=score,
last_frame=frame_id,
velocity=torch.zeros_like(bbox),
acc_frame=0,
)
# backdrop update according to IoU
backdrop_inds = torch.nonzero(ids == -1, as_tuple=False).squeeze(1)
ious = bbox_overlaps(bboxes[backdrop_inds], bboxes)
for i, ind in enumerate(backdrop_inds):
if (ious[i, :ind] > self.nms_backdrop_iou_thr).any():
backdrop_inds[i] = -1
backdrop_inds = backdrop_inds[backdrop_inds > -1]
# old backdrops would be removed at first
self.backdrops.insert(
0,
dict(
bboxes=bboxes[backdrop_inds],
embeds=embeds[backdrop_inds],
labels=labels[backdrop_inds],
),
)
# pop memo
invalid_ids = []
for k, v in self.tracks.items():
if frame_id - v["last_frame"] >= self.memo_tracklet_frames:
invalid_ids.append(k)
for invalid_id in invalid_ids:
self.tracks.pop(invalid_id)
if len(self.backdrops) > self.memo_backdrop_frames:
self.backdrops.pop()
@property
def memo(self) -> Tuple[Tensor, ...]:
"""Get tracks memory."""
memo_embeds = []
memo_ids = []
memo_bboxes = []
memo_labels = []
# velocity of tracks
memo_vs = []
# get tracks
for k, v in self.tracks.items():
memo_bboxes.append(v["bbox"][None, :])
memo_embeds.append(v["embed"][None, :])
memo_ids.append(k)
memo_labels.append(v["label"].view(1, 1))
memo_vs.append(v["velocity"][None, :])
memo_ids = torch.tensor(memo_ids, dtype=torch.long).view(1, -1)
# get backdrops
for backdrop in self.backdrops:
backdrop_ids = torch.full(
(1, backdrop["embeds"].size(0)), -1, dtype=torch.long
)
backdrop_vs = torch.zeros_like(backdrop["bboxes"])
memo_bboxes.append(backdrop["bboxes"])
memo_embeds.append(backdrop["embeds"])
memo_ids = torch.cat([memo_ids, backdrop_ids], dim=1)
memo_labels.append(backdrop["labels"][:, None])
memo_vs.append(backdrop_vs)
memo_bboxes = torch.cat(memo_bboxes, dim=0)
memo_embeds = torch.cat(memo_embeds, dim=0)
memo_labels = torch.cat(memo_labels, dim=0).squeeze(1)
memo_vs = torch.cat(memo_vs, dim=0)
return memo_bboxes, memo_labels, memo_embeds, memo_ids.squeeze(0), memo_vs
def track(
self,
model: torch.nn.Module,
img: torch.Tensor,
feats: List[torch.Tensor],
data_sample: TrackDataSample,
rescale=True,
with_segm=False,
**kwargs
) -> InstanceData:
"""Tracking forward function.
Args:
model (nn.Module): MOT model.
img (Tensor): of shape (T, C, H, W) encoding input image.
Typically these should be mean centered and std scaled.
The T denotes the number of key images and usually is 1 in
QDTrack method.
feats (list[Tensor]): Multi level feature maps of `img`.
data_sample (:obj:`TrackDataSample`): The data sample.
It includes information such as `pred_instances`.
rescale (bool, optional): If True, the bounding boxes should be
rescaled to fit the original scale of the image. Defaults to
True.
Returns:
:obj:`InstanceData`: Tracking results of the input images.
Each InstanceData usually contains ``bboxes``, ``labels``,
``scores`` and ``instances_id``.
"""
metainfo = data_sample.metainfo
bboxes = data_sample.pred_instances.bboxes
labels = data_sample.pred_instances.labels
scores = data_sample.pred_instances.scores
frame_id = metainfo.get("frame_id", -1)
# create pred_track_instances
pred_track_instances = InstanceData()
# return zero bboxes if there is no track targets
if bboxes.shape[0] == 0:
ids = torch.zeros_like(labels)
pred_track_instances = data_sample.pred_instances.clone()
pred_track_instances.instances_id = ids
return pred_track_instances
# get track feats
rescaled_bboxes = bboxes.clone()
if rescale:
scale_factor = rescaled_bboxes.new_tensor(metainfo["scale_factor"]).repeat(
(1, 2)
)
rescaled_bboxes = rescaled_bboxes * scale_factor
track_feats = model.track_head.predict(feats, [rescaled_bboxes])
# sort according to the object_score
_, inds = scores.sort(descending=True)
bboxes = bboxes[inds]
scores = scores[inds]
labels = labels[inds]
embeds = track_feats[inds, :]
if with_segm:
mask_inds = torch.arange(bboxes.size(0)).to(embeds.device)
mask_inds = mask_inds[inds]
else:
mask_inds = []
# duplicate removal for potential backdrops and cross classes
valids = bboxes.new_ones((bboxes.size(0)))
ious = bbox_overlaps(bboxes, bboxes)
for i in range(1, bboxes.size(0)):
thr = (
self.nms_backdrop_iou_thr
if scores[i] < self.obj_score_thr
else self.nms_class_iou_thr
)
if (ious[i, :i] > thr).any():
valids[i] = 0
valids = valids == 1
bboxes = bboxes[valids]
scores = scores[valids]
labels = labels[valids]
embeds = embeds[valids, :]
if with_segm:
mask_inds = mask_inds[valids]
# init ids container
ids = torch.full((bboxes.size(0),), -1, dtype=torch.long)
# match if buffer is not empty
if bboxes.size(0) > 0 and not self.empty:
(memo_bboxes, memo_labels, memo_embeds, memo_ids, memo_vs) = self.memo
if self.match_metric == "bisoftmax":
feats = torch.mm(embeds, memo_embeds.t())
d2t_scores = feats.softmax(dim=1)
t2d_scores = feats.softmax(dim=0)
match_scores = (d2t_scores + t2d_scores) / 2
elif self.match_metric == "softmax":
feats = torch.mm(embeds, memo_embeds.t())
match_scores = feats.softmax(dim=1)
elif self.match_metric == "cosine":
match_scores = torch.mm(
F.normalize(embeds, p=2, dim=1),
F.normalize(memo_embeds, p=2, dim=1).t(),
)
else:
raise NotImplementedError
# track with the same category
if self.with_cats:
cat_same = labels.view(-1, 1) == memo_labels.view(1, -1)
match_scores *= cat_same.float().to(match_scores.device)
# track according to match_scores
for i in range(bboxes.size(0)):
conf, memo_ind = torch.max(match_scores[i, :], dim=0)
id = memo_ids[memo_ind]
if conf > self.match_score_thr:
if id > -1:
# keep bboxes with high object score
# and remove background bboxes
if scores[i] > self.obj_score_thr:
ids[i] = id
match_scores[:i, memo_ind] = 0
match_scores[i + 1 :, memo_ind] = 0
else:
if conf > self.nms_conf_thr:
ids[i] = -2
# initialize new tracks
new_inds = (ids == -1) & (scores > self.init_score_thr).cpu()
num_news = new_inds.sum()
ids[new_inds] = torch.arange(
self.num_tracks, self.num_tracks + num_news, dtype=torch.long
)
self.num_tracks += num_news
self.update(ids, bboxes, embeds, labels, scores, frame_id)
tracklet_inds = ids > -1
# update pred_track_instances
pred_track_instances.bboxes = bboxes[tracklet_inds]
pred_track_instances.labels = labels[tracklet_inds]
pred_track_instances.scores = scores[tracklet_inds]
pred_track_instances.instances_id = ids[tracklet_inds]
if with_segm:
pred_track_instances.mask_inds = mask_inds[tracklet_inds]
return pred_track_instances
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