[Refactor] Refactor structure of cylinder3d

configs/_base_/models/cylinder3d.py

@@ -1,4 +1,5 @@
 grid_shape = [480, 360, 32]
+point_cloud_range = [0, -3.14159265359, -4, 50, 3.14159265359, 2]
 model = dict(
     type='Cylinder3D',
     data_preprocessor=dict(
@@ -7,18 +8,17 @@
         voxel_type='cylindrical',
         voxel_layer=dict(
             grid_shape=grid_shape,
-            point_cloud_range=[0, -3.14159265359, -4, 50, 3.14159265359, 2],
+            point_cloud_range=point_cloud_range,
             max_num_points=-1,
-            max_voxels=-1,
-        ),
-    ),
+            max_voxels=-1)),
     voxel_encoder=dict(
         type='SegVFE',
-        feat_channels=[64, 128, 256, 256],
         in_channels=6,
+        feat_channels=[64, 128, 256, 256],
         with_voxel_center=True,
-        feat_compression=16,
-        return_point_feats=False),
+        grid_shape=grid_shape,
+        point_cloud_range=point_cloud_range,
+        feat_compression=16),
     backbone=dict(
         type='Asymm3DSpconv',
         grid_size=grid_shape,
@@ -29,13 +29,14 @@
         type='Cylinder3DHead',
         channels=128,
         num_classes=20,
+        dropout_ratio=0,
         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'),
-    ),
+        conv_seg_kernel_size=3,
+        ignore_index=19),
     train_cfg=None,
-    test_cfg=dict(mode='whole'),
-)
+    test_cfg=dict(mode='whole'))

configs/_base_/models/dgcnn.py

@@ -19,7 +19,7 @@
         conv_cfg=dict(type='Conv1d'),
         norm_cfg=dict(type='BN1d'),
         act_cfg=dict(type='LeakyReLU', negative_slope=0.2),
-        loss_decode=dict(
+        loss_ce=dict(
             type='mmdet.CrossEntropyLoss',
             use_sigmoid=False,
             class_weight=None,  # modified with dataset

configs/_base_/models/minkunet.py

@@ -27,7 +27,7 @@
         channels=96,
         num_classes=19,
         dropout_ratio=0,
-        loss_decode=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True),
+        loss_ce=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True),
         ignore_index=19),
     train_cfg=dict(),
     test_cfg=dict())

configs/_base_/models/paconv_ssg.py

@@ -37,7 +37,7 @@
         conv_cfg=dict(type='Conv1d'),
         norm_cfg=dict(type='BN1d'),
         act_cfg=dict(type='ReLU'),
-        loss_decode=dict(
+        loss_ce=dict(
             type='mmdet.CrossEntropyLoss',
             use_sigmoid=False,
             class_weight=None,  # should be modified with dataset

configs/_base_/models/pointnet2_ssg.py

@@ -26,7 +26,7 @@
         conv_cfg=dict(type='Conv1d'),
         norm_cfg=dict(type='BN1d'),
         act_cfg=dict(type='ReLU'),
-        loss_decode=dict(
+        loss_ce=dict(
             type='mmdet.CrossEntropyLoss',
             use_sigmoid=False,
             class_weight=None,  # should be modified with dataset

configs/_base_/models/spvcnn.py

@@ -28,7 +28,7 @@
         channels=96,
         num_classes=19,
         dropout_ratio=0,
-        loss_decode=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True),
+        loss_ce=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True),
         ignore_index=19),
     train_cfg=dict(),
     test_cfg=dict())

configs/cylinder3d/cylinder3d_4xb4-3x_semantickitti.py

@@ -27,12 +27,12 @@
         gamma=0.1)
 ]
 
-train_dataloader = dict(batch_size=4, )
+train_dataloader = 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 = dict(checkpoint=dict(type='CheckpointHook', interval=5))
+default_hooks = dict(checkpoint=dict(type='CheckpointHook', interval=1))

configs/dgcnn/dgcnn_4xb32-cosine-100e_s3dis-seg_test-area5.py

@@ -8,7 +8,7 @@
     backbone=dict(in_channels=9),  # [xyz, rgb, normalized_xyz]
     decode_head=dict(
         num_classes=13, ignore_index=13,
-        loss_decode=dict(class_weight=None)),  # S3DIS doesn't use class_weight
+        loss_ce=dict(class_weight=None)),  # S3DIS doesn't use class_weight
     test_cfg=dict(
         num_points=4096,
         block_size=1.0,

configs/paconv/paconv_ssg-cuda_8xb8-cosine-200e_s3dis-seg.py

@@ -7,7 +7,7 @@
 model = dict(
     decode_head=dict(
         num_classes=13, ignore_index=13,
-        loss_decode=dict(class_weight=None)),  # S3DIS doesn't use class_weight
+        loss_ce=dict(class_weight=None)),  # S3DIS doesn't use class_weight
     test_cfg=dict(
         num_points=4096,
         block_size=1.0,

configs/paconv/paconv_ssg_8xb8-cosine-150e_s3dis-seg.py

@@ -7,7 +7,7 @@
 model = dict(
     decode_head=dict(
         num_classes=13, ignore_index=13,
-        loss_decode=dict(class_weight=None)),  # S3DIS doesn't use class_weight
+        loss_ce=dict(class_weight=None)),  # S3DIS doesn't use class_weight
     test_cfg=dict(
         num_points=4096,
         block_size=1.0,

configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg-xyz-only.py

@@ -13,7 +13,7 @@
         # `data/scannet/seg_info/train_label_weight.npy`
         # you can copy paste the values here, or input the file path as
         # `class_weight=data/scannet/seg_info/train_label_weight.npy`
-        loss_decode=dict(class_weight=[
+        loss_ce=dict(class_weight=[
             2.389689, 2.7215734, 4.5944676, 4.8543367, 4.096086, 4.907941,
             4.690836, 4.512031, 4.623311, 4.9242644, 5.358117, 5.360071,
             5.019636, 4.967126, 5.3502126, 5.4023647, 5.4027233, 5.4169416,

configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py

@@ -12,7 +12,7 @@
         # `data/scannet/seg_info/train_label_weight.npy`
         # you can copy paste the values here, or input the file path as
         # `class_weight=data/scannet/seg_info/train_label_weight.npy`
-        loss_decode=dict(class_weight=[
+        loss_ce=dict(class_weight=[
             2.389689, 2.7215734, 4.5944676, 4.8543367, 4.096086, 4.907941,
             4.690836, 4.512031, 4.623311, 4.9242644, 5.358117, 5.360071,
             5.019636, 4.967126, 5.3502126, 5.4023647, 5.4027233, 5.4169416,

configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py

@@ -8,7 +8,7 @@
     backbone=dict(in_channels=9),  # [xyz, rgb, normalized_xyz]
     decode_head=dict(
         num_classes=13, ignore_index=13,
-        loss_decode=dict(class_weight=None)),  # S3DIS doesn't use class_weight
+        loss_ce=dict(class_weight=None)),  # S3DIS doesn't use class_weight
     test_cfg=dict(
         num_points=4096,
         block_size=1.0,

configs/pointnet2/pointnet2_ssg_2xb16-cosine-200e_scannet-seg-xyz-only.py

@@ -13,7 +13,7 @@
         # `data/scannet/seg_info/train_label_weight.npy`
         # you can copy paste the values here, or input the file path as
         # `class_weight=data/scannet/seg_info/train_label_weight.npy`
-        loss_decode=dict(class_weight=[
+        loss_ce=dict(class_weight=[
             2.389689, 2.7215734, 4.5944676, 4.8543367, 4.096086, 4.907941,
             4.690836, 4.512031, 4.623311, 4.9242644, 5.358117, 5.360071,
             5.019636, 4.967126, 5.3502126, 5.4023647, 5.4027233, 5.4169416,

configs/pointnet2/pointnet2_ssg_2xb16-cosine-200e_scannet-seg.py

@@ -12,7 +12,7 @@
         # `data/scannet/seg_info/train_label_weight.npy`
         # you can copy paste the values here, or input the file path as
         # `class_weight=data/scannet/seg_info/train_label_weight.npy`
-        loss_decode=dict(class_weight=[
+        loss_ce=dict(class_weight=[
             2.389689, 2.7215734, 4.5944676, 4.8543367, 4.096086, 4.907941,
             4.690836, 4.512031, 4.623311, 4.9242644, 5.358117, 5.360071,
             5.019636, 4.967126, 5.3502126, 5.4023647, 5.4027233, 5.4169416,

configs/pointnet2/pointnet2_ssg_2xb16-cosine-50e_s3dis-seg.py

@@ -8,7 +8,7 @@
     backbone=dict(in_channels=9),  # [xyz, rgb, normalized_xyz]
     decode_head=dict(
         num_classes=13, ignore_index=13,
-        loss_decode=dict(class_weight=None)),  # S3DIS doesn't use class_weight
+        loss_ce=dict(class_weight=None)),  # S3DIS doesn't use class_weight
     test_cfg=dict(
         num_points=4096,
         block_size=1.0,

mmdet3d/configs/_base_/models/cylinder3d.py

@@ -7,6 +7,7 @@
 from mmdet3d.models.voxel_encoders import SegVFE
 
 grid_shape = [480, 360, 32]
+point_cloud_range = [0, -3.14159265359, -4, 50, 3.14159265359, 2]
 model = dict(
     type=Cylinder3D,
     data_preprocessor=dict(
@@ -15,18 +16,17 @@
         voxel_type='cylindrical',
         voxel_layer=dict(
             grid_shape=grid_shape,
-            point_cloud_range=[0, -3.14159265359, -4, 50, 3.14159265359, 2],
+            point_cloud_range=point_cloud_range,
             max_num_points=-1,
-            max_voxels=-1,
-        ),
-    ),
+            max_voxels=-1)),
     voxel_encoder=dict(
         type=SegVFE,
-        feat_channels=[64, 128, 256, 256],
         in_channels=6,
+        feat_channels=[64, 128, 256, 256],
         with_voxel_center=True,
-        feat_compression=16,
-        return_point_feats=False),
+        grid_shape=grid_shape,
+        point_cloud_range=point_cloud_range,
+        feat_compression=16),
     backbone=dict(
         type=Asymm3DSpconv,
         grid_size=grid_shape,
@@ -37,13 +37,14 @@
         type=Cylinder3DHead,
         channels=128,
         num_classes=20,
+        dropout_ratio=0,
         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'),
-    ),
+        conv_seg_kernel_size=3,
+        ignore_index=19),
     train_cfg=None,
-    test_cfg=dict(mode='whole'),
-)
+    test_cfg=dict(mode='whole'))

mmdet3d/configs/_base_/models/minkunet.py

@@ -34,7 +34,7 @@
         channels=96,
         num_classes=19,
         dropout_ratio=0,
-        loss_decode=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True),
+        loss_ce=dict(type='mmdet.CrossEntropyLoss', avg_non_ignore=True),
         ignore_index=19),
     train_cfg=dict(),
     test_cfg=dict())

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

@@ -32,7 +32,7 @@
         gamma=0.1)
 ]
 
-train_dataloader.update(dict(batch_size=4, ))
+train_dataloader.update(dict(batch_size=4))
 
 # Default setting for scaling LR automatically
 #   - `enable` means enable scaling LR automatically

mmdet3d/models/data_preprocessors/data_preprocessor.py

@@ -16,7 +16,7 @@
 from mmdet3d.structures.det3d_data_sample import SampleList
 from mmdet3d.utils import OptConfigType
 from .utils import multiview_img_stack_batch
-from .voxelize import VoxelizationByGridShape, dynamic_scatter_3d
+from .voxelize import VoxelizationByGridShape
 
 
 @MODELS.register_module()
@@ -393,7 +393,7 @@ def voxelize(self, points: List[Tensor],
             coors = torch.cat(coors, dim=0)
         elif self.voxel_type == 'cylindrical':
             voxels, coors = [], []
-            for i, (res, data_sample) in enumerate(zip(points, data_samples)):
+            for i, res in enumerate(points):
                 rho = torch.sqrt(res[:, 0]**2 + res[:, 1]**2)
                 phi = torch.atan2(res[:, 1], res[:, 0])
                 polar_res = torch.stack((rho, phi, res[:, 2]), dim=-1)
@@ -416,7 +416,6 @@ def voxelize(self, points: List[Tensor],
                 res_coors = torch.floor(
                     (polar_res_clamp - min_bound) / polar_res_clamp.new_tensor(
                         self.voxel_layer.voxel_size)).int()
-                self.get_voxel_seg(res_coors, data_sample)
                 res_coors = F.pad(res_coors, (1, 0), mode='constant', value=i)
                 res_voxels = torch.cat((polar_res, res[:, :2], res[:, 3:]),
                                        dim=-1)
@@ -467,30 +466,6 @@ def voxelize(self, points: List[Tensor],
 
         return voxel_dict
 
-    def get_voxel_seg(self, res_coors: Tensor,
-                      data_sample: SampleList) -> None:
-        """Get voxel-wise segmentation label and point2voxel map.
-
-        Args:
-            res_coors (Tensor): The voxel coordinates of points, Nx3.
-            data_sample: (:obj:`Det3DDataSample`): The annotation data of
-                every samples. Add voxel-wise annotation forsegmentation.
-        """
-
-        if self.training:
-            pts_semantic_mask = data_sample.gt_pts_seg.pts_semantic_mask
-            voxel_semantic_mask, _, point2voxel_map = dynamic_scatter_3d(
-                F.one_hot(pts_semantic_mask.long()).float(), res_coors, 'mean',
-                True)
-            voxel_semantic_mask = torch.argmax(voxel_semantic_mask, dim=-1)
-            data_sample.gt_pts_seg.voxel_semantic_mask = voxel_semantic_mask
-            data_sample.point2voxel_map = point2voxel_map
-        else:
-            pseudo_tensor = res_coors.new_ones([res_coors.shape[0], 1]).float()
-            _, _, point2voxel_map = dynamic_scatter_3d(pseudo_tensor,
-                                                       res_coors, 'mean', True)
-            data_sample.point2voxel_map = point2voxel_map
-
     def ravel_hash(self, x: np.ndarray) -> np.ndarray:
         """Get voxel coordinates hash for np.unique.