little changes

mmdet3d/models/middle_encoders/sparse_unet.py

@@ -224,8 +224,7 @@ class SparseUNet(nn.Module):
                     kernel_size,
                     padding=padding,
                     bias=False,
-                    indice_key=indice_key),
-            )
+                    indice_key=indice_key))
         elif conv_type == 'spconv':
             m = spconv.SparseSequential(
                 build_norm_layer(norm_cfg, in_channels)[1],
@@ -237,8 +236,7 @@ class SparseUNet(nn.Module):
                     stride=stride,
                     padding=padding,
                     bias=False,
-                    indice_key=indice_key),
-            )
+                    indice_key=indice_key))
         elif conv_type == 'inverseconv':
             m = spconv.SparseSequential(
                 build_norm_layer(norm_cfg, in_channels)[1],
@@ -248,8 +246,7 @@ class SparseUNet(nn.Module):
                     out_channels,
                     kernel_size,
                     bias=False,
-                    indice_key=indice_key),
-            )
+                    indice_key=indice_key))
         else:
             raise NotImplementedError
         return m
@@ -290,8 +287,7 @@ class SparseUNet(nn.Module):
                     bias=False,
                     indice_key=indice_key),
                 build_norm_layer(norm_cfg, out_channels)[1],
-                nn.ReLU(inplace=True),
-            )
+                nn.ReLU(inplace=True))
         elif conv_type == 'spconv':
             m = spconv.SparseSequential(
                 spconv.SparseConv3d(
@@ -303,8 +299,7 @@ class SparseUNet(nn.Module):
                     bias=False,
                     indice_key=indice_key),
                 build_norm_layer(norm_cfg, out_channels)[1],
-                nn.ReLU(inplace=True),
-            )
+                nn.ReLU(inplace=True))
         elif conv_type == 'inverseconv':
             m = spconv.SparseSequential(
                 spconv.SparseInverseConv3d(

mmdet3d/ops/sparse_block.py

@@ -5,9 +5,7 @@ from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
 from mmdet.ops import build_norm_layer
 from mmdet.ops.conv import conv_cfg
 
-conv_cfg.update({
-    'SubMConv3d': spconv.SubMConv3d,
-})
+conv_cfg.update({'SubMConv3d': spconv.SubMConv3d})
 
 
 def conv3x3(in_planes, out_planes, stride=1, indice_key=None):

tests/test_roiaware_pool3d.py

@@ -19,23 +19,10 @@ def test_RoIAwarePool3d():
         dtype=torch.float32).cuda(
         )  # boxes (m, 7) with bottom center in lidar coordinate
     pts = torch.tensor(
-        [
-            [1, 2, 3.3],
-            [1.2, 2.5, 3.0],
-            [0.8, 2.1, 3.5],
-            [1.6, 2.6, 3.6],
-            [0.8, 1.2, 3.9],
-            [-9.2, 21.0, 18.2],
-            [3.8, 7.9, 6.3],
-            [4.7, 3.5, -12.2],
-            [3.8, 7.6, -2],
-            [-10.6, -12.9, -20],
-            [-16, -18, 9],
-            [-21.3, -52, -5],
-            [0, 0, 0],
-            [6, 7, 8],
-            [-2, -3, -4],
-        ],
+        [[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6],
+         [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3],
+         [4.7, 3.5, -12.2], [3.8, 7.6, -2], [-10.6, -12.9, -20], [-16, -18, 9],
+         [-21.3, -52, -5], [0, 0, 0], [6, 7, 8], [-2, -3, -4]],
         dtype=torch.float32).cuda()  # points (n, 3) in lidar coordinate
     pts_feature = pts.clone()
 
@@ -83,23 +70,10 @@ def test_points_in_boxes_cpu():
         dtype=torch.float32
     )  # boxes (m, 7) with bottom center in lidar coordinate
     pts = torch.tensor(
-        [
-            [1, 2, 3.3],
-            [1.2, 2.5, 3.0],
-            [0.8, 2.1, 3.5],
-            [1.6, 2.6, 3.6],
-            [0.8, 1.2, 3.9],
-            [-9.2, 21.0, 18.2],
-            [3.8, 7.9, 6.3],
-            [4.7, 3.5, -12.2],
-            [3.8, 7.6, -2],
-            [-10.6, -12.9, -20],
-            [-16, -18, 9],
-            [-21.3, -52, -5],
-            [0, 0, 0],
-            [6, 7, 8],
-            [-2, -3, -4],
-        ],
+        [[1, 2, 3.3], [1.2, 2.5, 3.0], [0.8, 2.1, 3.5], [1.6, 2.6, 3.6],
+         [0.8, 1.2, 3.9], [-9.2, 21.0, 18.2], [3.8, 7.9, 6.3],
+         [4.7, 3.5, -12.2], [3.8, 7.6, -2], [-10.6, -12.9, -20], [-16, -18, 9],
+         [-21.3, -52, -5], [0, 0, 0], [6, 7, 8], [-2, -3, -4]],
         dtype=torch.float32)  # points (n, 3) in lidar coordinate
 
     point_indices = points_in_boxes_cpu(points=pts, boxes=boxes)
@@ -109,9 +83,3 @@ def test_points_in_boxes_cpu():
         dtype=torch.int32)
     assert point_indices.shape == torch.Size([2, 15])
     assert (point_indices == expected_point_indices).all()
-
-
-if __name__ == '__main__':
-    test_points_in_boxes_cpu()
-    test_points_in_boxes_gpu()
-    test_RoIAwarePool3d()