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softgroup_default_scannet.yaml
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GENERAL:
task: train # train, test
manual_seed: 123
model_dir: model/softgroup/softgroup.py
dataset_dir: data/scannetv2_inst.py
DATA:
data_root: dataset
dataset: scannetv2
filename_suffix: _inst_nostuff.pth
semantic_classes: 20
classes: 18
class_numpoint_mean: [-1., -1., 3917., 12056., 2303.,
8331., 3948., 3166., 5629., 11719.,
1003., 3317., 4912., 10221., 3889.,
4136., 2120., 945., 3967., 2589.]
ignore_label: -100
input_channel: 3
scale: 50 # voxel_size = 1 / scale, scale 50 -> voxel_size 0.02m
batch_size: 4
full_scale: [128, 512]
max_npoint: 250000
mode: 4 # 4=mean
STRUCTURE:
model_name: softgroup
width: 32
block_residual: True
block_reps: 2
use_coords: True
semantic_only: False
TRAIN:
epochs: 500
train_workers: 4 # data loader workers
optim: Adam # Adam or SGD
lr: 0.001
step_epoch: 200
multiplier: 0.5
momentum: 0.9
weight_decay: 0.0001
save_freq: 16 # also eval_freq
loss_weight: [1.0, 1.0, 1.0, 1.0, 1.0] # semantic_loss, offset_norm_loss, cls_loss, mask_loss, score_loss
fg_thresh: 1.
bg_thresh: 0.
score_scale: 50 # the minimal voxel size is 2cm
score_fullscale: 20
score_mode: 4 # mean
pretrain_path: 'hais_ckpt.pth'
pretrain_module: ['input_conv', 'unet', 'output_layer', 'semantic_linear', 'offset_linear', 'intra_ins_unet', 'intra_ins_outputlayer']
fix_module: ['input_conv', 'unet', 'output_layer', 'semantic_linear', 'offset_linear']
point_aggr_radius: 0.04
cluster_shift_meanActive: 300
prepare_epochs: -1
max_proposal_num: 200
iou_thr: 0.5
score_thr: 0.2
TEST:
split: val
test_epoch: 500
test_workers: 16
test_seed: 567
using_NMS: False
TEST_NMS_THRESH: 0.3
TEST_SCORE_THRESH: -1
TEST_NPOINT_THRESH: 100
eval: True
save_semantic: False
save_pt_offsets: False
save_instance: False
test_mask_score_thre: -0.5 # bias fg << bg