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[CodeCamp2023-516]Add new configuration files for Cylinder3D (#2681)
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‎mmdet3d/configs/_base_/models/cylinder3d.py

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# Copyright (c) OpenMMLab. All rights reserved.
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from mmdet3d.models import Cylinder3D
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from mmdet3d.models.backbones import Asymm3DSpconv
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from mmdet3d.models.data_preprocessors import Det3DDataPreprocessor
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from mmdet3d.models.decode_heads.cylinder3d_head import Cylinder3DHead
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from mmdet3d.models.losses import LovaszLoss
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from mmdet3d.models.voxel_encoders import SegVFE
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grid_shape = [480, 360, 32]
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model = dict(
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type=Cylinder3D,
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data_preprocessor=dict(
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type=Det3DDataPreprocessor,
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voxel=True,
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voxel_type='cylindrical',
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voxel_layer=dict(
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grid_shape=grid_shape,
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point_cloud_range=[0, -3.14159265359, -4, 50, 3.14159265359, 2],
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max_num_points=-1,
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max_voxels=-1,
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),
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),
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voxel_encoder=dict(
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type=SegVFE,
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feat_channels=[64, 128, 256, 256],
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in_channels=6,
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with_voxel_center=True,
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feat_compression=16,
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return_point_feats=False),
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backbone=dict(
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type=Asymm3DSpconv,
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grid_size=grid_shape,
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input_channels=16,
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base_channels=32,
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norm_cfg=dict(type='BN1d', eps=1e-5, momentum=0.1)),
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decode_head=dict(
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type=Cylinder3DHead,
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channels=128,
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num_classes=20,
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loss_ce=dict(
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type='mmdet.CrossEntropyLoss',
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use_sigmoid=False,
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class_weight=None,
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loss_weight=1.0),
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loss_lovasz=dict(type=LovaszLoss, loss_weight=1.0, reduction='none'),
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),
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train_cfg=None,
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test_cfg=dict(mode='whole'),
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)

‎mmdet3d/configs/cylinder3d/__init__.py

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# Copyright (c) OpenMMLab. All rights reserved.

‎mmdet3d/configs/cylinder3d/cylinder3d_4xb4-3x_semantickitti.py

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# Copyright (c) OpenMMLab. All rights reserved.
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from mmengine import read_base
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with read_base():
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from .._base_.datasets.semantickitti import *
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from .._base_.models.cylinder3d import *
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from .._base_.default_runtime import *
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from mmengine.optim.optimizer.optimizer_wrapper import OptimWrapper
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from mmengine.optim.scheduler.lr_scheduler import LinearLR, MultiStepLR
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from mmengine.runner.loops import EpochBasedTrainLoop, TestLoop, ValLoop
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from torch.optim import AdamW
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# optimizer
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lr = 0.001
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optim_wrapper = dict(
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type=OptimWrapper, optimizer=dict(type=AdamW, lr=lr, weight_decay=0.01))
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train_cfg = dict(type=EpochBasedTrainLoop, max_epochs=36, val_interval=1)
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val_cfg = dict(type=ValLoop)
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test_cfg = dict(type=TestLoop)
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# learning rate
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param_scheduler = [
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dict(type=LinearLR, start_factor=0.001, by_epoch=False, begin=0, end=1000),
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dict(
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type=MultiStepLR,
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begin=0,
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end=36,
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by_epoch=True,
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milestones=[30],
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gamma=0.1)
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]
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train_dataloader.update(dict(batch_size=4, ))
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# Default setting for scaling LR automatically
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# - `enable` means enable scaling LR automatically
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# or not by default.
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# - `base_batch_size` = (8 GPUs) x (4 samples per GPU).
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# auto_scale_lr = dict(enable=False, base_batch_size=32)
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default_hooks.update(dict(checkpoint=dict(type=CheckpointHook, interval=5)))

‎mmdet3d/configs/cylinder3d/cylinder3d_8xb2-laser-polar-mix-3x_semantickitti.py

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# Copyright (c) OpenMMLab. All rights reserved.
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from mmengine import read_base
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with read_base():
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from .._base_.datasets.semantickitti import *
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from .._base_.default_runtime import *
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from .._base_.models.cylinder3d import *
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from .._base_.schedules.schedule_3x import *
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from mmcv.transforms.wrappers import RandomChoice
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from mmdet3d.datasets.transforms.transforms_3d import LaserMix, PolarMix
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train_pipeline = [
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dict(type=LoadPointsFromFile, coord_type='LIDAR', load_dim=4, use_dim=4),
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dict(
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type=LoadAnnotations3D,
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with_bbox_3d=False,
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with_label_3d=False,
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with_seg_3d=True,
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seg_3d_dtype='np.int32',
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seg_offset=2**16,
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dataset_type='semantickitti'),
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dict(type=PointSegClassMapping),
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dict(
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type=RandomChoice,
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transforms=[
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[
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dict(
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type=LaserMix,
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num_areas=[3, 4, 5, 6],
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pitch_angles=[-25, 3],
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pre_transform=[
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dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4),
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dict(
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type=LoadAnnotations3D,
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with_bbox_3d=False,
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with_label_3d=False,
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with_seg_3d=True,
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seg_3d_dtype='np.int32',
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seg_offset=2**16,
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dataset_type='semantickitti'),
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dict(type=PointSegClassMapping)
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],
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prob=1)
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],
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[
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dict(
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type=PolarMix,
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instance_classes=[0, 1, 2, 3, 4, 5, 6, 7],
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swap_ratio=0.5,
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rotate_paste_ratio=1.0,
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pre_transform=[
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dict(
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type=LoadPointsFromFile,
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coord_type='LIDAR',
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load_dim=4,
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use_dim=4),
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dict(
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type=LoadAnnotations3D,
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with_bbox_3d=False,
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with_label_3d=False,
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with_seg_3d=True,
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seg_3d_dtype='np.int32',
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seg_offset=2**16,
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dataset_type='semantickitti'),
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dict(type=PointSegClassMapping)
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],
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prob=1)
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],
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],
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prob=[0.5, 0.5]),
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dict(
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type=GlobalRotScaleTrans,
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rot_range=[0., 6.28318531],
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scale_ratio_range=[0.95, 1.05],
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translation_std=[0, 0, 0],
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),
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dict(type=Pack3DDetInputs, keys=['points', 'pts_semantic_mask'])
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]
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train_dataloader.update(dict(dataset=dict(pipeline=train_pipeline)))
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default_hooks.update(dict(checkpoint=dict(type=CheckpointHook, interval=1)))

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