[CodeCamp2023-516]Add new configuration files for Cylinder3D (#2681)

mmdet3d/configs/_base_/models/cylinder3d.py

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

mmdet3d/configs/cylinder3d/__init__.py

+# Copyright (c) OpenMMLab. All rights reserved.

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

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

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

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