cleanup from review

theano/tensor/nnet/corr.py

@@ -75,33 +75,13 @@ class BaseCorrMM(gof.Op):
             str(self.subsample),
             str(self.filter_dilation))
 
-    def cast(self, in1, in2):
+    @staticmethod
+    def as_common_dtype(in1, in2):
         """
         Upcast input variables if neccesary.
         """
-        float_types = ['float32', 'float64']
-        dtype_1 = in1.type.dtype
-        dtype_2 = in2.type.dtype
-
-        assert dtype_1 in float_types
-        assert dtype_2 in float_types
-
-        if dtype_1 == 'float64' or dtype_2 == 'float64':
-            dtype_o = 'float64'
-        else:
-            dtype_o = 'float32'
-
-        if dtype_1 != dtype_o:
-            out1 = in1.astype(dtype_o)
-        else:
-            out1 = in1
-
-        if dtype_2 != dtype_o:
-            out2 = in2.astype(dtype_o)
-        else:
-            out2 = in2
-
-        return out1, out2
+        dtype = theano.scalar.upcast(in1.dtype, in2.dtype)
+        return in1.astype(dtype), in2.astype(dtype)
 
     def c_support_code(self):
         return blas_header_text()
@@ -411,7 +391,7 @@ class CorrMM(BaseCorrMM):
     def make_node(self, img, kern):
         img = as_tensor_variable(img)
         kern = as_tensor_variable(kern)
-        img, kern = self.cast(img, kern)
+        img, kern = self.as_common_dtype(img, kern)
         if img.type.ndim != 4:
             raise TypeError('img must be 4D tensor')
         if kern.type.ndim != 4:
@@ -474,7 +454,7 @@ class CorrMM_gradWeights(BaseCorrMM):
     def make_node(self, img, topgrad, shape=None):
         img = as_tensor_variable(img)
         topgrad = as_tensor_variable(topgrad)
-        img, topgrad = self.cast(img, topgrad)
+        img, topgrad = self.as_common_dtype(img, topgrad)
         if img.type.ndim != 4:
             raise TypeError('img must be 4D tensor')
         if topgrad.type.ndim != 4:
@@ -576,7 +556,7 @@ class CorrMM_gradInputs(BaseCorrMM):
     def make_node(self, kern, topgrad, shape=None):
         kern = as_tensor_variable(kern)
         topgrad = as_tensor_variable(topgrad)
-        kern, topgrad = self.cast(kern, topgrad)
+        kern, topgrad = self.as_common_dtype(kern, topgrad)
         if kern.type.ndim != 4:
             raise TypeError('kern must be 4D tensor')
         if topgrad.type.ndim != 4: