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_base_ = [ |
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'../_base_/datasets/scannet-3d.py', '../_base_/models/groupfree3d.py', |
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'../_base_/schedules/schedule-3x.py', '../_base_/default_runtime.py' |
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] |
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|
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model = dict( |
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backbone=dict( |
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type='PointNet2SASSG', |
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in_channels=3, |
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num_points=(2048, 1024, 512, 256), |
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radius=(0.2, 0.4, 0.8, 1.2), |
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num_samples=(64, 32, 16, 16), |
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sa_channels=((128, 128, 256), (256, 256, 512), (256, 256, 512), |
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(256, 256, 512)), |
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fp_channels=((512, 512), (512, 288)), |
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norm_cfg=dict(type='BN2d'), |
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sa_cfg=dict( |
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type='PointSAModule', |
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pool_mod='max', |
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use_xyz=True, |
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normalize_xyz=True)), |
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bbox_head=dict( |
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num_classes=18, |
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num_decoder_layers=12, |
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num_proposal=512, |
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size_cls_agnostic=False, |
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bbox_coder=dict( |
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type='GroupFree3DBBoxCoder', |
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num_sizes=18, |
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num_dir_bins=1, |
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with_rot=False, |
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size_cls_agnostic=False, |
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mean_sizes=[[0.76966727, 0.8116021, 0.92573744], |
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[1.876858, 1.8425595, 1.1931566], |
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[0.61328, 0.6148609, 0.7182701], |
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[1.3955007, 1.5121545, 0.83443564], |
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[0.97949594, 1.0675149, 0.6329687], |
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[0.531663, 0.5955577, 1.7500148], |
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[0.9624706, 0.72462326, 1.1481868], |
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[0.83221924, 1.0490936, 1.6875663], |
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[0.21132214, 0.4206159, 0.5372846], |
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[1.4440073, 1.8970833, 0.26985747], |
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[1.0294262, 1.4040797, 0.87554324], |
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[1.3766412, 0.65521795, 1.6813129], |
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[0.6650819, 0.71111923, 1.298853], |
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[0.41999173, 0.37906948, 1.7513971], |
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[0.59359556, 0.5912492, 0.73919016], |
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[0.50867593, 0.50656086, 0.30136237], |
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[1.1511526, 1.0546296, 0.49706793], |
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[0.47535285, 0.49249494, 0.5802117]]), |
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sampling_objectness_loss=dict( |
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type='mmdet.FocalLoss', |
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use_sigmoid=True, |
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gamma=2.0, |
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alpha=0.25, |
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loss_weight=8.0), |
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objectness_loss=dict( |
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type='mmdet.FocalLoss', |
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use_sigmoid=True, |
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gamma=2.0, |
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alpha=0.25, |
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loss_weight=1.0), |
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center_loss=dict( |
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type='mmdet.SmoothL1Loss', |
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beta=0.04, |
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reduction='sum', |
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loss_weight=10.0), |
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dir_class_loss=dict( |
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type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0), |
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dir_res_loss=dict( |
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type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=10.0), |
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size_class_loss=dict( |
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type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0), |
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size_res_loss=dict( |
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type='mmdet.SmoothL1Loss', |
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beta=1.0 / 9.0, |
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reduction='sum', |
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loss_weight=10.0 / 9.0), |
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semantic_loss=dict( |
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type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0)), |
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test_cfg=dict( |
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sample_mode='kps', |
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nms_thr=0.25, |
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score_thr=0.0, |
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per_class_proposal=True, |
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prediction_stages='last_three')) |
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|
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dataset_type = 'ScanNetDataset' |
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data_root = './data/scannet/' |
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class_names = ('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window', |
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'bookshelf', 'picture', 'counter', 'desk', 'curtain', |
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'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub', |
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'garbagebin') |
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|
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metainfo = dict(classes=class_names) |
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backend_args = None |
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|
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train_pipeline = [ |
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dict( |
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type='LoadPointsFromFile', |
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coord_type='DEPTH', |
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load_dim=6, |
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use_dim=[0, 1, 2], |
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backend_args=backend_args), |
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dict( |
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type='LoadAnnotations3D', |
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with_bbox_3d=True, |
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with_label_3d=True, |
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with_mask_3d=True, |
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with_seg_3d=True, |
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backend_args=backend_args), |
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dict(type='GlobalAlignment', rotation_axis=2), |
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dict(type='PointSegClassMapping'), |
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dict(type='PointSample', num_points=50000), |
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dict( |
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type='RandomFlip3D', |
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sync_2d=False, |
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flip_ratio_bev_horizontal=0.5, |
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flip_ratio_bev_vertical=0.5), |
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dict( |
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type='GlobalRotScaleTrans', |
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rot_range=[-0.087266, 0.087266], |
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scale_ratio_range=[1.0, 1.0]), |
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dict( |
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type='Pack3DDetInputs', |
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keys=[ |
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'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask', |
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'pts_instance_mask' |
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]) |
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] |
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test_pipeline = [ |
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dict( |
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type='LoadPointsFromFile', |
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coord_type='DEPTH', |
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load_dim=6, |
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use_dim=[0, 1, 2], |
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backend_args=backend_args), |
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dict(type='GlobalAlignment', rotation_axis=2), |
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dict( |
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type='MultiScaleFlipAug3D', |
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img_scale=(1333, 800), |
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pts_scale_ratio=1, |
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flip=False, |
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transforms=[ |
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dict( |
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type='GlobalRotScaleTrans', |
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rot_range=[0, 0], |
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scale_ratio_range=[1., 1.], |
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translation_std=[0, 0, 0]), |
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dict( |
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type='RandomFlip3D', |
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sync_2d=False, |
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flip_ratio_bev_horizontal=0.5, |
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flip_ratio_bev_vertical=0.5), |
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dict(type='PointSample', num_points=50000), |
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]), |
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dict(type='Pack3DDetInputs', keys=['points']) |
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] |
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|
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train_dataloader = dict( |
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batch_size=8, |
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num_workers=4, |
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sampler=dict(type='DefaultSampler', shuffle=True), |
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dataset=dict( |
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type='RepeatDataset', |
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times=5, |
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dataset=dict( |
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type=dataset_type, |
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data_root=data_root, |
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ann_file='scannet_infos_train.pkl', |
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pipeline=train_pipeline, |
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filter_empty_gt=False, |
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metainfo=metainfo, |
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|
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|
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box_type_3d='Depth', |
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backend_args=backend_args))) |
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val_dataloader = dict( |
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batch_size=1, |
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num_workers=1, |
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sampler=dict(type='DefaultSampler', shuffle=False), |
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dataset=dict( |
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type=dataset_type, |
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data_root=data_root, |
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ann_file='scannet_infos_val.pkl', |
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pipeline=test_pipeline, |
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metainfo=metainfo, |
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test_mode=True, |
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box_type_3d='Depth', |
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backend_args=backend_args)) |
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test_dataloader = dict( |
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batch_size=1, |
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num_workers=1, |
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sampler=dict(type='DefaultSampler', shuffle=False), |
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dataset=dict( |
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type=dataset_type, |
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data_root=data_root, |
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ann_file='scannet_infos_val.pkl', |
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pipeline=test_pipeline, |
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metainfo=metainfo, |
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test_mode=True, |
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box_type_3d='Depth', |
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backend_args=backend_args)) |
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val_evaluator = dict(type='IndoorMetric') |
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test_evaluator = val_evaluator |
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|
|
|
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lr = 0.006 |
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optim_wrapper = dict( |
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type='OptimWrapper', |
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optimizer=dict(type='AdamW', lr=lr, weight_decay=0.0005), |
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clip_grad=dict(max_norm=0.1, norm_type=2), |
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paramwise_cfg=dict( |
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custom_keys={ |
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'bbox_head.decoder_layers': dict(lr_mult=0.1, decay_mult=1.0), |
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'bbox_head.decoder_self_posembeds': dict( |
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lr_mult=0.1, decay_mult=1.0), |
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'bbox_head.decoder_cross_posembeds': dict( |
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lr_mult=0.1, decay_mult=1.0), |
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'bbox_head.decoder_query_proj': dict(lr_mult=0.1, decay_mult=1.0), |
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'bbox_head.decoder_key_proj': dict(lr_mult=0.1, decay_mult=1.0) |
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})) |
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|
|
|
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param_scheduler = [ |
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dict( |
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type='MultiStepLR', |
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begin=0, |
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end=80, |
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by_epoch=True, |
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milestones=[56, 68], |
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gamma=0.1) |
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] |
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|
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train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=80, val_interval=1) |
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val_cfg = dict(type='ValLoop') |
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test_cfg = dict(type='TestLoop') |
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|
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default_hooks = dict( |
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checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=10)) |
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