mirror of
https://github.com/botastic/SoftGroup.git
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73 lines
1.6 KiB
YAML
73 lines
1.6 KiB
YAML
model:
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channels: 32
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num_blocks: 7
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semantic_classes: 20
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instance_classes: 18
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sem2ins_classes: []
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semantic_only: False
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ignore_label: -100
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grouping_cfg:
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score_thr: 0.2
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radius: 0.04
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mean_active: 300
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class_numpoint_mean: [-1., -1., 3917., 12056., 2303.,
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8331., 3948., 3166., 5629., 11719.,
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1003., 3317., 4912., 10221., 3889.,
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4136., 2120., 945., 3967., 2589.]
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npoint_thr: 0.05 # absolute if class_numpoint == -1, relative if class_numpoint != -1
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ignore_classes: [0, 1]
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instance_voxel_cfg:
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scale: 50
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spatial_shape: 20
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train_cfg:
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max_proposal_num: 200
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pos_iou_thr: 0.5
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test_cfg:
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x4_split: False
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cls_score_thr: 0.001
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mask_score_thr: -0.5
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min_npoint: 100
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fixed_modules: ['input_conv', 'unet', 'output_layer', 'semantic_linear', 'offset_linear']
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data:
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train:
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type: 'scannetv2'
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data_root: 'dataset/scannetv2'
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prefix: 'train'
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suffix: '_inst_nostuff.pth'
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training: True
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voxel_cfg:
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scale: 50
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spatial_shape: [128, 512]
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max_npoint: 250000
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min_npoint: 5000
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test:
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type: 'scannetv2'
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data_root: 'dataset/scannetv2'
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prefix: 'val'
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suffix: '_inst_nostuff.pth'
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training: False
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voxel_cfg:
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scale: 50
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spatial_shape: [128, 512]
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max_npoint: 250000
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min_npoint: 5000
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dataloader:
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train:
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batch_size: 4
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num_workers: 4
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test:
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batch_size: 1
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num_workers: 16
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optimizer:
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type: 'Adam'
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lr: 0.001
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epochs: 512
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step_epoch: 200
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save_freq: 8
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pretrain: 'hais_ckpt.pth'
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work_dir: 'work_dirs/softgroup_scannet'
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