config.yaml

BATCH_NORM:     True
BATCH_SIZE:     2
USE_XYZ:        True
USE_MLP:        False
repeat_time:    1
USE_WEIGHT:     True
device:         'cuda'

opt_level:     "O2"
MAX_POINTS_NUM: 120000
USE_MULTI_GPU:  True
use_ASPP:       False
NormalizedXYZ:  False
K_forward:      8
K_propagate:    8
K_self:         8
nn_interpolate: 3
point_dim:      3
total_epoches: 100

grid_size:      [0.05, 0.1, 0.2, 0.4, 0.8, 0.8]
base_dim:       48

dialated_rate:  [1, 1, 1, 1, 1, 4]
resblocks:      [3, 3, 3, 3, 3, 3]
resblocks_back: [0, 0, 0, 0, 0, 0]

train_data_path: '/media/xingyi/Tools4TB/PointConv_ScanNet/ScanNet_withNormal/train/*.pth'
val_data_path:   '/media/xingyi/Tools4TB/PointConv_ScanNet/ScanNet_withNormal/val/*.pth'
test_data_path:  '/media/xingyi/Tools4TB/PointConv_ScanNet/ScanNet_withNormal/test/*.pth'
pretrain:        null
optimizer:       'Adam'
learning_rate:   0.001
decay_rate:      0.0001
multi_gpu:       null

model_name:      'scannet_pointConv'
experiment_dir:  './experiment/'
test_dir:        './eval_experiment/'
num_classes:      20

classes:         ['wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table', 'door',
                  'window', 'bookshelf', 'picture', 'counter', 'desk', 'curtain',
                  'refridgerator', 'shower curtain', 'toilet', 'sink', 'bathtub', 'otherfurniture']