change flops to support grouped convolution

theano/tensor/nnet/abstract_conv.py

@@ -1495,20 +1495,17 @@ class BaseAbstractConv(Op):
     def flops(self, inp, outp):
         """ Useful with the hack in profiling to print the MFlops"""
         if self.convdim == 2:
-            if self.num_groups > 1:
-                raise NotImplementedError(
-                    'flops not implemented for grouped convolution')
             # if the output shape is correct, then this gives the correct
             # flops for any direction, sampling, padding, and border mode
             inputs, filters = inp
             outputs, = outp
-            assert inputs[1] == filters[1]
+            assert inputs[1] == (filters[1] * self.num_groups)
             # nb mul and add by output pixel
             flops = filters[2] * filters[3] * 2
             # nb flops by output image
             flops *= outputs[2] * outputs[3]
             # nb patch multiplied
-            flops *= inputs[1] * filters[0] * inputs[0]
+            flops *= inputs[1] * filters[0] * inputs[0] / self.num_groups
             return flops
         else:
             # TODO implement for convdim == 3