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# Copyright (c) OpenMMLab. All rights reserved.
from typing import List, Optional
from mmengine.model import BaseModule
from torch import Tensor
from mmdet.registry import MODELS, TASK_UTILS
from mmdet.structures import TrackSampleList
from mmdet.structures.bbox import bbox2roi
from mmdet.utils import InstanceList
@MODELS.register_module()
class QuasiDenseTrackHead(BaseModule):
"""The quasi-dense track head."""
def __init__(self,
roi_extractor: Optional[dict] = None,
embed_head: Optional[dict] = None,
regress_head: Optional[dict] = None,
train_cfg: Optional[dict] = None,
test_cfg: Optional[dict] = None,
init_cfg: Optional[dict] = None,
**kwargs):
super().__init__(init_cfg=init_cfg)
self.train_cfg = train_cfg
self.test_cfg = test_cfg
if embed_head is not None:
self.init_embed_head(roi_extractor, embed_head)
if regress_head is not None:
raise NotImplementedError('Regression head is not supported yet.')
self.init_assigner_sampler()
def init_embed_head(self, roi_extractor, embed_head) -> None:
"""Initialize ``embed_head``
Args:
roi_extractor (dict, optional): Configuration of roi extractor.
Defaults to None.
embed_head (dict, optional): Configuration of embed head. Defaults
to None.
"""
self.roi_extractor = MODELS.build(roi_extractor)
self.embed_head = MODELS.build(embed_head)
def init_assigner_sampler(self) -> None:
"""Initialize assigner and sampler."""
self.bbox_assigner = None
self.bbox_sampler = None
if self.train_cfg:
self.bbox_assigner = TASK_UTILS.build(self.train_cfg.assigner)
self.bbox_sampler = TASK_UTILS.build(
self.train_cfg.sampler, default_args=dict(context=self))
@property
def with_track(self) -> bool:
"""bool: whether the multi-object tracker has an embed head"""
return hasattr(self, 'embed_head') and self.embed_head is not None
def extract_roi_feats(self, feats: List[Tensor],
bboxes: List[Tensor]) -> Tensor:
"""Extract roi features.
Args:
feats (list[Tensor]): list of multi-level image features.
bboxes (list[Tensor]): list of bboxes in sampling result.
Returns:
Tensor: The extracted roi features.
"""
rois = bbox2roi(bboxes)
bbox_feats = self.roi_extractor(feats[:self.roi_extractor.num_inputs],
rois)
return bbox_feats
def loss(self, key_feats: List[Tensor], ref_feats: List[Tensor],
rpn_results_list: InstanceList,
ref_rpn_results_list: InstanceList, data_samples: TrackSampleList,
**kwargs) -> dict:
"""Calculate losses from a batch of inputs and data samples.
Args:
key_feats (list[Tensor]): list of multi-level image features.
ref_feats (list[Tensor]): list of multi-level ref_img features.
rpn_results_list (list[:obj:`InstanceData`]): List of region
proposals of key img.
ref_rpn_results_list (list[:obj:`InstanceData`]): List of region
proposals of ref img.
data_samples (list[:obj:`TrackDataSample`]): The batch
data samples. It usually includes information such
as `gt_instance`.
Returns:
dict: A dictionary of loss components.
"""
assert self.with_track
num_imgs = len(data_samples)
batch_gt_instances = []
ref_batch_gt_instances = []
batch_gt_instances_ignore = []
gt_match_indices_list = []
for track_data_sample in data_samples:
key_data_sample = track_data_sample.get_key_frames()[0]
ref_data_sample = track_data_sample.get_ref_frames()[0]
batch_gt_instances.append(key_data_sample.gt_instances)
ref_batch_gt_instances.append(ref_data_sample.gt_instances)
if 'ignored_instances' in key_data_sample:
batch_gt_instances_ignore.append(
key_data_sample.ignored_instances)
else:
batch_gt_instances_ignore.append(None)
# get gt_match_indices
ins_ids = key_data_sample.gt_instances.instances_ids.tolist()
ref_ins_ids = ref_data_sample.gt_instances.instances_ids.tolist()
match_indices = Tensor([
ref_ins_ids.index(i) if (i in ref_ins_ids and i > 0) else -1
for i in ins_ids
]).to(key_feats[0].device)
gt_match_indices_list.append(match_indices)
key_sampling_results, ref_sampling_results = [], []
for i in range(num_imgs):
rpn_results = rpn_results_list[i]
ref_rpn_results = ref_rpn_results_list[i]
# rename ref_rpn_results.bboxes to ref_rpn_results.priors
ref_rpn_results.priors = ref_rpn_results.pop('bboxes')
assign_result = self.bbox_assigner.assign(
rpn_results, batch_gt_instances[i],
batch_gt_instances_ignore[i])
sampling_result = self.bbox_sampler.sample(
assign_result,
rpn_results,
batch_gt_instances[i],
feats=[lvl_feat[i][None] for lvl_feat in key_feats])
key_sampling_results.append(sampling_result)
ref_assign_result = self.bbox_assigner.assign(
ref_rpn_results, ref_batch_gt_instances[i],
batch_gt_instances_ignore[i])
ref_sampling_result = self.bbox_sampler.sample(
ref_assign_result,
ref_rpn_results,
ref_batch_gt_instances[i],
feats=[lvl_feat[i][None] for lvl_feat in ref_feats])
ref_sampling_results.append(ref_sampling_result)
key_bboxes = [res.pos_bboxes for res in key_sampling_results]
key_roi_feats = self.extract_roi_feats(key_feats, key_bboxes)
ref_bboxes = [res.bboxes for res in ref_sampling_results]
ref_roi_feats = self.extract_roi_feats(ref_feats, ref_bboxes)
loss_track = self.embed_head.loss(key_roi_feats, ref_roi_feats,
key_sampling_results,
ref_sampling_results,
gt_match_indices_list)
return loss_track
def predict(self, feats: List[Tensor],
rescaled_bboxes: List[Tensor]) -> Tensor:
"""Perform forward propagation of the tracking head and predict
tracking results on the features of the upstream network.
Args:
feats (list[Tensor]): Multi level feature maps of `img`.
rescaled_bboxes (list[Tensor]): list of rescaled bboxes in sampling
result.
Returns:
Tensor: The extracted track features.
"""
bbox_feats = self.extract_roi_feats(feats, rescaled_bboxes)
track_feats = self.embed_head.predict(bbox_feats)
return track_feats
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