Reconstruct centerpoint_rpn.

mmdet3d/models/necks/__init__.py

@@ -1,5 +1,5 @@
 from mmdet.models.necks.fpn import FPN
-from .centerpoint_rpn import CenterPointRPN
+from .centerpoint_fpn import CenterPointFPN
 from .second_fpn import SECONDFPN
 
-__all__ = ['FPN', 'SECONDFPN', 'CenterPointRPN']
+__all__ = ['FPN', 'SECONDFPN', 'CenterPointFPN']

tests/test_necks.py

@@ -1,20 +1,31 @@
 import torch
 
-from mmdet3d.models.builder import build_neck
+from mmdet3d.models.builder import build_backbone, build_neck
 
 
 def test_centerpoint_rpn():
-    centerpoint_rpn_cfg = dict(
-        type='CenterPointRPN',
+    second_cfg = dict(
+        type='SECOND',
+        in_channels=64,
+        out_channels=[64, 128, 256],
         layer_nums=[3, 5, 5],
-        downsample_strides=[2, 2, 2],
-        downsample_channels=[64, 128, 256],
+        layer_strides=[2, 2, 2],
+        norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
+        conv_cfg=dict(type='Conv2d', bias=False))
+
+    second = build_backbone(second_cfg)
+
+    centerpoint_fpn_cfg = dict(
+        type='CenterPointFPN',
+        in_channels=[64, 128, 256],
+        out_channels=[128, 128, 128],
         upsample_strides=[0.5, 1, 2],
-        upsample_channels=[128, 128, 128],
-        input_channels=64,
-        norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01))
-    centerpoint_rpn = build_neck(centerpoint_rpn_cfg)
-    centerpoint_rpn.init_weights()
+        norm_cfg=dict(type='BN', eps=1e-3, momentum=0.01),
+        upsample_cfg=dict(type='deconv', bias=False))
+
+    centerpoint_fpn = build_neck(centerpoint_fpn_cfg)
+
     input = torch.rand([4, 64, 512, 512])
-    output = centerpoint_rpn(input)
+    sec_output = second(input)
+    output = centerpoint_fpn(sec_output)
     assert output.shape == torch.Size([4, 384, 128, 128])