diff --git a/convert_torch.py b/convert_torch.py
index b639f73..479ce2f 100644
--- a/convert_torch.py
+++ b/convert_torch.py
@@ -149,6 +149,11 @@ def lua_recursive_model(module,seq):
             n = LambdaReduce(lambda x,y,dim=dim: torch.cat((x,y),dim))
             lua_recursive_model(m,n)
             add_submodule(seq,n)
+        elif name == 'SpatialDilatedConvolution':
+            if not hasattr(m,'groups') or m.groups is None: m.groups=1
+            n = nn.Conv2d(m.nInputPlane,m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),(m.dilationW,m.dilationH),m.groups,bias=(m.bias is not None))
+            copy_param(m,n)
+            add_submodule(seq,n)
         elif name == 'TorchObject':
             print('Not Implement',name,real._typename)
         else:
@@ -168,6 +173,10 @@ def lua_recursive_source(module):
             if not hasattr(m,'groups') or m.groups is None: m.groups=1
             s += ['nn.Conv2d({},{},{},{},{},{},{},bias={}),#Conv2d'.format(m.nInputPlane,
                 m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),1,m.groups,m.bias is not None)]
+        elif name == 'SpatialDilatedConvolution':
+            if not hasattr(m,'groups') or m.groups is None: m.groups=1
+            s += ['nn.Conv2d({},{},{},{},{},{},{},bias={}),#Conv2d'.format(m.nInputPlane,
+                m.nOutputPlane,(m.kW,m.kH),(m.dW,m.dH),(m.padW,m.padH),(m.dilationW,m.dilationH),m.groups,m.bias is not None)]
         elif name == 'SpatialBatchNormalization':
             s += ['nn.BatchNorm2d({},{},{},{}),#BatchNorm2d'.format(m.running_mean.size(0), m.eps, m.momentum, m.affine)]
         elif name == 'VolumetricBatchNormalization':