h3dnet_8xb3_scannet-seg.py

_base_ = [
    '../_base_/datasets/scannet-3d.py', '../_base_/models/h3dnet.py',
    '../_base_/schedules/schedule-3x.py', '../_base_/default_runtime.py'
]

# model settings
model = dict(
    rpn_head=dict(
        num_classes=18,
        bbox_coder=dict(
            type='PartialBinBasedBBoxCoder',
            num_sizes=18,
            num_dir_bins=24,
            with_rot=False,
            mean_sizes=[[0.76966727, 0.8116021, 0.92573744],
                        [1.876858, 1.8425595, 1.1931566],
                        [0.61328, 0.6148609, 0.7182701],
                        [1.3955007, 1.5121545, 0.83443564],
                        [0.97949594, 1.0675149, 0.6329687],
                        [0.531663, 0.5955577, 1.7500148],
                        [0.9624706, 0.72462326, 1.1481868],
                        [0.83221924, 1.0490936, 1.6875663],
                        [0.21132214, 0.4206159, 0.5372846],
                        [1.4440073, 1.8970833, 0.26985747],
                        [1.0294262, 1.4040797, 0.87554324],
                        [1.3766412, 0.65521795, 1.6813129],
                        [0.6650819, 0.71111923, 1.298853],
                        [0.41999173, 0.37906948, 1.7513971],
                        [0.59359556, 0.5912492, 0.73919016],
                        [0.50867593, 0.50656086, 0.30136237],
                        [1.1511526, 1.0546296, 0.49706793],
                        [0.47535285, 0.49249494, 0.5802117]])),
    roi_head=dict(
        bbox_head=dict(
            num_classes=18,
            bbox_coder=dict(
                type='PartialBinBasedBBoxCoder',
                num_sizes=18,
                num_dir_bins=24,
                with_rot=False,
                mean_sizes=[[0.76966727, 0.8116021, 0.92573744],
                            [1.876858, 1.8425595, 1.1931566],
                            [0.61328, 0.6148609, 0.7182701],
                            [1.3955007, 1.5121545, 0.83443564],
                            [0.97949594, 1.0675149, 0.6329687],
                            [0.531663, 0.5955577, 1.7500148],
                            [0.9624706, 0.72462326, 1.1481868],
                            [0.83221924, 1.0490936, 1.6875663],
                            [0.21132214, 0.4206159, 0.5372846],
                            [1.4440073, 1.8970833, 0.26985747],
                            [1.0294262, 1.4040797, 0.87554324],
                            [1.3766412, 0.65521795, 1.6813129],
                            [0.6650819, 0.71111923, 1.298853],
                            [0.41999173, 0.37906948, 1.7513971],
                            [0.59359556, 0.5912492, 0.73919016],
                            [0.50867593, 0.50656086, 0.30136237],
                            [1.1511526, 1.0546296, 0.49706793],
                            [0.47535285, 0.49249494, 0.5802117]]))))

train_dataloader = dict(
    batch_size=3,
    num_workers=2,
)

# yapf:disable
default_hooks = dict(
    logger=dict(type='LoggerHook', interval=30)
)
# yapf:enable
# Default setting for scaling LR automatically
#   - `enable` means enable scaling LR automatically
#       or not by default.
#   - `base_batch_size` = (8 GPUs) x (3 samples per GPU).
auto_scale_lr = dict(enable=False, base_batch_size=24)