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82 lines
4.5 KiB
Markdown
82 lines
4.5 KiB
Markdown
```
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model:
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channels: 32 # number of base channel for the backbone network
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num_blocks: 7 # number of backbone blocks
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semantic_classes: 20 # number of semantic classes
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instance_classes: 18 # number of instance classes (semantic class can be less than instance classes)
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sem2ins_classes: [] # class index to get instance directly from semantic. This trick is applied on S3DIS floor and ceil since 1 scene ussualy contain only 1 floor and 1 ceil.
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semantic_only: False # If true, only the point-wise networks (semantic and offset) are trained. The top-down refinement stage is not taken into consideration. Set it to True ussualy using for pretraining the backbone.
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ignore_label: -100 # Ignore label while training
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grouping_cfg:
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score_thr: 0.2 # soft score threshold using in softgroup.
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radius: 0.04 # the search radius of K-nearest neighbor using for grouping.
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mean_active: 300 # using to constrain the total size after K-NN
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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.] # the mean number of point per instance for each class.
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npoint_thr: 0.05 # absolute if class_numpoint == -1, relative if class_numpoint != -1 # when grouping, if num_point[i] > npoint_thr * class_numpoint_mean[i], the cluster is consider as an instance, otherwise discarded.
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ignore_classes: [0, 1] # classes will be ignored while perform grouping.
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instance_voxel_cfg:
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scale: 50 # scaling factor, voxel size = 1 / scale. In this case voxel_size = 1/50 = 0.02m
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spatial_shape: 20 # the dimension of instance in terms of voxels, i.e., H, W, D of instance will be 20 voxels.
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train_cfg:
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max_proposal_num: 200 # if number of proposals > max_proposal_num while training, the number of propsoals will be truncated to reduce memory usage.
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pos_iou_thr: 0.5 # intersection over union threshold to identify positive and negative samples.
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test_cfg:
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x4_split: False # whether divide the scene into 4 part then merge the results. This is used for S3DIS dataset since the scene is very big.
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cls_score_thr: 0.001 # score threshold for postprocessing
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mask_score_thr: -0.5 # threshold to classify background and foreground in segmentation
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min_npoint: 100 # min number of points for each instance
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fixed_modules: ['input_conv', 'unet', 'output_layer', 'semantic_linear', 'offset_linear'] # These module will not have gradient updates while training.
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data:
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train:
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type: 'scannetv2' # dataset type
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data_root: 'dataset/scannetv2' # root path to your data
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prefix: 'train' # data prefix
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suffix: '_inst_nostuff.pth' # data suffix
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training: True # training mode
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repeat: 4 # repeat factor for the data. In case the dataset is small, using repeat to avoid data loading every epoch -> reduce loading time.
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voxel_cfg:
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scale: 50 # scaling factor, voxel size = 1 / scale. In this case voxel_size = 1/50 = 0.02m
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spatial_shape: [128, 512] # min and max spatial shape of the whole scene after random crop
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max_npoint: 250000 # max number of points after random crop
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min_npoint: 5000 # min number of points after random crop
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test:
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type: 'scannetv2' # test data type
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data_root: 'dataset/scannetv2' # test data root
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prefix: 'val' # data prefix
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suffix: '_inst_nostuff.pth' # data suffix
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training: False # test mode
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voxel_cfg:
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scale: 50 # scaling factor, voxel size = 1 / scale. In this case voxel_size = 1/50 = 0.02m
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spatial_shape: [128, 512] # no effect during testing
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max_npoint: 250000 # no effect during testing
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min_npoint: 5000 # no effect during testing
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dataloader:
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train:
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batch_size: 4 # train batch size
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num_workers: 4 # train number of processes to load data
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test:
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batch_size: 1 # test batch size
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num_workers: 1 # test number of processes to load data
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optimizer:
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type: 'Adam' # optimizer type
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lr: 0.004 # learning rate
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save_cfg:
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semantic: True # weather saving semantic while evaluation
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offset: True # weather saving offset while evaluation
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instance: True # weather saving instance while evaluation
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fp16: False # Mix precision training
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epochs: 128 # Number of total epochs
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step_epoch: 50 # Epoch to step learning rate
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save_freq: 4 # frequency to save model and perform validation
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pretrain: './hais_ckpt_spconv2.pth' # pretrain model path
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work_dir: '' # directory to save model and log
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```
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