add group norm support

mmdet/models/bbox_heads/convfc_bbox_head.py

 import torch.nn as nn
 
 from .bbox_head import BBoxHead
-from ..utils import ConvModule
+from ..utils import ConvModule, build_norm_layer
 
 
 class ConvFCBBoxHead(BBoxHead):
@@ -113,8 +113,13 @@ class ConvFCBBoxHead(BBoxHead):
             for i in range(num_branch_fcs):
                 fc_in_channels = (last_layer_dim
                                   if i == 0 else self.fc_out_channels)
-                branch_fcs.append(
-                    nn.Linear(fc_in_channels, self.fc_out_channels))
+                if self.normalize is not None:
+                    branch_fcs.append(nn.Sequential(
+                        nn.Linear(fc_in_channels, self.fc_out_channels, False),
+                        build_norm_layer(self.normalize, self.fc_out_channels)))
+                else:
+                    branch_fcs.append(
+                        nn.Linear(fc_in_channels, self.fc_out_channels))
             last_layer_dim = self.fc_out_channels
         return branch_convs, branch_fcs, last_layer_dim
 
@@ -124,7 +129,8 @@ class ConvFCBBoxHead(BBoxHead):
             for m in module_list.modules():
                 if isinstance(m, nn.Linear):
                     nn.init.xavier_uniform_(m.weight)
-                    nn.init.constant_(m.bias, 0)
+                    if m.bias is not None:
+                        nn.init.constant_(m.bias, 0)
 
     def forward(self, x):
         # shared part

mmdet/models/necks/fpn.py

 import torch.nn as nn
 import torch.nn.functional as F
 from ..utils import ConvModule
-from ..utils import xavier_init
+from mmcv.cnn import xavier_init
 
 
 class FPN(nn.Module):

mmdet/models/utils/norm.py

 import torch.nn as nn
 
-norm_cfg = {'BN': nn.BatchNorm2d, 'SyncBN': None, 'GN': None}
+norm_cfg = {'BN': nn.BatchNorm2d, 'SyncBN': None, 'GN': nn.GroupNorm}
 
 
 def build_norm_layer(cfg, num_features):
@@ -9,9 +9,15 @@ def build_norm_layer(cfg, num_features):
     cfg_.setdefault('eps', 1e-5)
     layer_type = cfg_.pop('type')
 
+    # args name matching
+    if layer_type == 'GN':
+        cfg_.setdefault('num_channels', num_features)
+    else:
+        cfg_.setdefault('num_features', num_features)
+
     if layer_type not in norm_cfg:
         raise KeyError('Unrecognized norm type {}'.format(layer_type))
     elif norm_cfg[layer_type] is None:
         raise NotImplementedError
 
-    return norm_cfg[layer_type](num_features, **cfg_)
+    return norm_cfg[layer_type](**cfg_)