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s3dis_pointnet2_paconv.yaml
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s3dis_pointnet2_paconv.yaml
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DATA:
data_name: s3dis
data_root: dataset/s3dis
train_list: dataset/s3dis/list/train12346.txt
train_full_folder: dataset/s3dis/trainval_fullarea
val_list: dataset/s3dis/list/val5.txt
test_area: 5
classes: 13
fea_dim: 6 # point feature dimension
block_size: 1.0
stride_rate: 0.5
sample_rate: 1.0
num_point: 4096 # point number [default: 4096]
TRAIN:
arch: pointnet2_paconv_seg
use_xyz: True
sync_bn: True # adopt sync_bn or not
ignore_label: 255
train_gpu:
train_workers: 8 # data loader workers
train_batch_size: 16 # batch size for training
train_batch_size_val: 8 # batch size for validation during training, memory and speed tradeoff
base_lr: 0.05
epochs: 100
start_epoch: 0
step_epoch: 30
multiplier: 0.1
momentum: 0.9
weight_decay: 0.0001
manual_seed:
print_freq: 100
save_freq: 1
save_path: exp/s3dis/pointnet2_paconv/model
weight: # path to initial weight (default: none)
resume:
evaluate: True # evaluate on validation set, extra gpu memory needed and small batch_size_val is recommend
m: 16
paconv: [True, True, True, True, False, False, False, False]
score_input: ed7
kernel_input: neighbor
hidden: [16, 16, 16]
no_transformation: False
color_augment: 0.0
norm_no_trans: True
correlation_loss: True
correlation_loss_scale: 10.0
TEST:
test_list: dataset/s3dis/list/val5.txt
test_list_full: dataset/s3dis/list/val5_full.txt
split: val # split in [train, val and test]
test_gpu: [0]
test_workers: 4
test_batch_size: 8
model_path: exp/s3dis/pointnet2_paconv/model/best_train.pth
save_folder: exp/s3dis/pointnet2_paconv/result/best_epoch/val5_0.5 # results save folder
names_path: data/s3dis/s3dis_names.txt