fix some bugs, add group divisible check

mmdet/models/backbones/resnext.py

@@ -53,6 +53,7 @@ class Bottleneck(_Bottleneck):
                 groups=groups,
                 bias=False)
         else:
+            groups = self.dcn.get('groups', 1)
             deformable_groups = self.dcn.get('deformable_groups', 1)
             if not self.with_modulated_dcn:
                 conv_op = DeformConv
@@ -194,7 +195,8 @@ class ResNeXt(ResNet):
                 base_width=self.base_width,
                 style=self.style,
                 with_cp=self.with_cp,
-                normalize=self.normalize)
+                normalize=self.normalize,
+                dcn=self.dcn)
             self.inplanes = planes * self.block.expansion
             layer_name = 'layer{}'.format(i + 1)
             self.add_module(layer_name, res_layer)

mmdet/ops/dcn/modules/deform_conv.py

@@ -21,6 +21,13 @@ class DeformConv(nn.Module):
                  bias=False):
         assert not bias
         super(DeformConv, self).__init__()
+
+        assert in_channels % groups == 0, \
+            'in_channels {} cannot be divisible by groups {}'.format(
+                in_channels, groups)
+        assert out_channels % groups == 0, \
+            'out_channels {} cannot be divisible by groups {}'.format(
+                out_channels, groups)
         self.in_channels = in_channels
         self.out_channels = out_channels
         self.kernel_size = _pair(kernel_size)

mmdet/ops/dcn/src/deform_conv_cuda.cpp

@@ -601,6 +601,7 @@ void modulated_deform_conv_cuda_backward(at::Tensor input, at::Tensor weight,
 
         columns = columns.view({group, columns.size(0) / group, columns.size(1)});
         grad_weight = grad_weight.view({group, grad_weight.size(0) / group, grad_weight.size(1), grad_weight.size(2), grad_weight.size(3)});
+        if (with_bias)
             grad_bias = grad_bias.view({group, grad_bias.size(0) / group});
 
         for (int g = 0; g < group; g++){
@@ -612,6 +613,7 @@ void modulated_deform_conv_cuda_backward(at::Tensor input, at::Tensor weight,
 
         columns = columns.view({columns.size(0) * columns.size(1), columns.size(2)});
         grad_weight = grad_weight.view({grad_weight.size(0) * grad_weight.size(1), grad_weight.size(2), grad_weight.size(3), grad_weight.size(4)});
+        if (with_bias)
             grad_bias = grad_bias.view({grad_bias.size(0) * grad_bias.size(1)});
     }
     grad_output = grad_output.view({grad_output.size(0) * grad_output.size(1), grad_output.size(2), grad_output.size(3), grad_output.size(4)});