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- _base_ = [
- '../_base_/models/faster-rcnn_r50_fpn.py', '../_base_/default_runtime.py'
- ]
- detector = _base_.model
- detector.pop('data_preprocessor')
- detector['backbone'].update(
- dict(
- norm_cfg=dict(type='BN', requires_grad=False),
- style='caffe',
- init_cfg=dict(
- type='Pretrained',
- checkpoint='open-mmlab://detectron2/resnet50_caffe')))
- detector.rpn_head.loss_bbox.update(
- dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0))
- detector.rpn_head.bbox_coder.update(dict(clip_border=False))
- detector.roi_head.bbox_head.update(dict(num_classes=1))
- detector.roi_head.bbox_head.bbox_coder.update(dict(clip_border=False))
- detector['init_cfg'] = dict(
- type='Pretrained',
- checkpoint= # noqa: E251
- 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/'
- 'faster_rcnn_r50_fpn_1x_coco-person/'
- 'faster_rcnn_r50_fpn_1x_coco-person_20201216_175929-d022e227.pth'
- # noqa: E501
- )
- del _base_.model
- model = dict(
- type='QDTrack',
- data_preprocessor=dict(
- type='TrackDataPreprocessor',
- mean=[103.530, 116.280, 123.675],
- std=[1.0, 1.0, 1.0],
- bgr_to_rgb=False,
- pad_size_divisor=32),
- detector=detector,
- track_head=dict(
- type='QuasiDenseTrackHead',
- roi_extractor=dict(
- type='SingleRoIExtractor',
- roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
- out_channels=256,
- featmap_strides=[4, 8, 16, 32]),
- embed_head=dict(
- type='QuasiDenseEmbedHead',
- num_convs=4,
- num_fcs=1,
- embed_channels=256,
- norm_cfg=dict(type='GN', num_groups=32),
- loss_track=dict(type='MultiPosCrossEntropyLoss', loss_weight=0.25),
- loss_track_aux=dict(
- type='MarginL2Loss',
- neg_pos_ub=3,
- pos_margin=0,
- neg_margin=0.1,
- hard_mining=True,
- loss_weight=1.0)),
- loss_bbox=dict(type='L1Loss', loss_weight=1.0),
- train_cfg=dict(
- assigner=dict(
- type='MaxIoUAssigner',
- pos_iou_thr=0.7,
- neg_iou_thr=0.5,
- min_pos_iou=0.5,
- match_low_quality=False,
- ignore_iof_thr=-1),
- sampler=dict(
- type='CombinedSampler',
- num=256,
- pos_fraction=0.5,
- neg_pos_ub=3,
- add_gt_as_proposals=True,
- pos_sampler=dict(type='InstanceBalancedPosSampler'),
- neg_sampler=dict(type='RandomSampler')))),
- tracker=dict(
- type='QuasiDenseTracker',
- init_score_thr=0.9,
- obj_score_thr=0.5,
- match_score_thr=0.5,
- memo_tracklet_frames=30,
- memo_backdrop_frames=1,
- memo_momentum=0.8,
- nms_conf_thr=0.5,
- nms_backdrop_iou_thr=0.3,
- nms_class_iou_thr=0.7,
- with_cats=True,
- match_metric='bisoftmax'))
- # optimizer
- optim_wrapper = dict(
- type='OptimWrapper',
- optimizer=dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001),
- clip_grad=dict(max_norm=35, norm_type=2))
- # learning policy
- param_scheduler = [
- dict(type='MultiStepLR', begin=0, end=4, by_epoch=True, milestones=[3])
- ]
- # runtime settings
- train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=4, val_interval=4)
- val_cfg = dict(type='ValLoop')
- test_cfg = dict(type='TestLoop')
- default_hooks = dict(
- logger=dict(type='LoggerHook', interval=50),
- visualization=dict(type='TrackVisualizationHook', draw=False))
- vis_backends = [dict(type='LocalVisBackend')]
- visualizer = dict(
- type='TrackLocalVisualizer', vis_backends=vis_backends, name='visualizer')
- # custom hooks
- custom_hooks = [
- # Synchronize model buffers such as running_mean and running_var in BN
- # at the end of each epoch
- dict(type='SyncBuffersHook')
- ]
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