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| _base_ = [ | |
| '../sam-vitb.py', | |
| '../../datasets/tao/tao_dataset_v1.py', | |
| '../../default_runtime.py' | |
| ] | |
| default_scope = 'mmdet' | |
| detector = _base_.model | |
| detector['init_cfg'] = dict( | |
| type='Pretrained', | |
| checkpoint= 'saved_models/pretrain_weights/sam_vit_b_01ec64_mmdet.pth' | |
| # noqa: E501 | |
| ) | |
| detector['type'] = 'SamMasa' | |
| del _base_.model | |
| model = dict( | |
| type='MASA', | |
| freeze_detector=True, | |
| unified_backbone=True, | |
| load_public_dets = True, | |
| benchmark = 'tao', | |
| public_det_path = 'results/public_dets/tao_val_dets/teta_50_internms/detic_tao_val_det/', | |
| data_preprocessor=dict( | |
| type='TrackDataPreprocessor', | |
| # Image normalization parameters | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| bgr_to_rgb=True, | |
| # Image padding parameters | |
| pad_mask=False, # In instance segmentation, the mask needs to be padded | |
| pad_size_divisor=1024), # Padding the image to multiples of 32 | |
| detector=detector, | |
| masa_adapter=[ | |
| dict( | |
| type='SimpleFPN', | |
| in_channels=[768, 768, 768, 768], | |
| out_channels=256, | |
| use_residual=True, | |
| num_outs=5), | |
| dict( | |
| type='DyHead', | |
| in_channels=256, | |
| out_channels=256, | |
| num_blocks=3) | |
| ], | |
| rpn_head=dict( | |
| type='RPNHead', | |
| in_channels=256, | |
| feat_channels=256, | |
| anchor_generator=dict( | |
| type='AnchorGenerator', | |
| scales=[8], | |
| ratios=[0.5, 1.0, 2.0], | |
| strides=[4, 8, 16, 32, 64]), | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[.0, .0, .0, .0], | |
| target_stds=[1.0, 1.0, 1.0, 1.0]), | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | |
| loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0) | |
| ), | |
| roi_head=dict( | |
| type='StandardRoIHead', | |
| bbox_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]), | |
| bbox_head=dict( | |
| type='Shared2FCBBoxHead', | |
| in_channels=256, | |
| fc_out_channels=1024, | |
| roi_feat_size=7, | |
| num_classes=1, | |
| bbox_coder=dict( | |
| type='DeltaXYWHBBoxCoder', | |
| target_means=[0., 0., 0., 0.], | |
| target_stds=[0.1, 0.1, 0.2, 0.2]), | |
| reg_class_agnostic=True, | |
| loss_cls=dict( | |
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | |
| loss_bbox=dict(type='L1Loss', loss_weight=1.0))), | |
| # model training and testing settings | |
| train_cfg=dict( | |
| rpn=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.3, | |
| min_pos_iou=0.3, | |
| match_low_quality=True, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=256, | |
| pos_fraction=0.5, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=False), | |
| allowed_border=-1, | |
| pos_weight=-1, | |
| debug=False), | |
| rpn_proposal=dict( | |
| nms_pre=2000, | |
| max_per_img=1000, | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| min_bbox_size=0), | |
| rcnn=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.5, | |
| neg_iou_thr=0.5, | |
| min_pos_iou=0.5, | |
| match_low_quality=False, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='RandomSampler', | |
| num=512, | |
| pos_fraction=0.25, | |
| neg_pos_ub=-1, | |
| add_gt_as_proposals=True), | |
| pos_weight=-1, | |
| debug=False)), | |
| test_cfg=dict( | |
| rpn=dict( | |
| nms_pre=1000, | |
| max_per_img=1000, | |
| nms=dict(type='nms', iou_threshold=0.7), | |
| min_bbox_size=0), | |
| rcnn=dict( | |
| score_thr=0.02, | |
| # nms=dict(type='nms', iou_threshold=0.5), | |
| nms=dict(type='nms', | |
| iou_threshold=0.5, | |
| class_agnostic=True, | |
| split_thr=100000), | |
| max_per_img=50, | |
| mask_thr_binary=0.5) | |
| # soft-nms is also supported for rcnn testing | |
| # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) | |
| ), | |
| track_head=dict( | |
| type='MasaTrackHead', | |
| 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='UnbiasedContrastLoss', 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)), | |
| train_cfg=dict( | |
| assigner=dict( | |
| type='MaxIoUAssigner', | |
| pos_iou_thr=0.7, | |
| neg_iou_thr=0.3, | |
| min_pos_iou=0.5, | |
| match_low_quality=False, | |
| ignore_iof_thr=-1), | |
| sampler=dict( | |
| type='CombinedSampler', | |
| num=512, | |
| 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='MasaTaoTracker', | |
| init_score_thr=0.0001, | |
| obj_score_thr=0.0001, | |
| match_score_thr=0.5, | |
| memo_tracklet_frames=10, | |
| memo_momentum=0.8, | |
| with_cats=False, | |
| max_distance=-1, | |
| fps=1, | |
| ) | |
| ) | |
| test_pipeline = [ | |
| dict( | |
| type='TransformBroadcaster', | |
| transforms=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='Resize', | |
| scale=(1024, 1024), | |
| keep_ratio=True), | |
| dict(type='LoadTrackAnnotations') | |
| ]), | |
| dict(type='PackTrackInputs') | |
| ] | |
| # runtime settings | |
| train_dataloader = None | |
| train_cfg = None | |
| 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='MasaTrackLocalVisualizer', vis_backends=vis_backends, name='visualizer') | |
| auto_scale_lr = dict(enable=False, base_batch_size=16) | |
| val_dataloader = dict( | |
| dataset=dict( | |
| ann_file='data/tao/annotations/tao_val_lvis_v1_classes.json', | |
| pipeline=test_pipeline, | |
| ) | |
| ) | |
| test_dataloader = val_dataloader | |
| test_evaluator = dict( | |
| ann_file='data/tao/annotations/tao_val_lvis_v1_classes.json', | |
| outfile_prefix='results/masa_results/masa-sam-vitb-tao-test-detic-dets', | |
| ) | |