Normalize AbstractConv inputs types to not have nodes with mixed input types.

theano/tensor/nnet/abstract_conv.py

@@ -235,6 +235,11 @@ class AbstractConv2d(BaseAbstractConv2d):
                                              filter_flip)
 
     def make_node(self, img, kern):
+        # Normalize the inputs types
+        ktype = img.type.clone(dtype=kern.dtype,
+                               broadcastable=kern.broadcastable)
+        kern = ktype.filter_variable(kern)
+
         if img.type.ndim != 4:
             raise TypeError('img must be 4D tensor')
         if kern.type.ndim != 4:
@@ -328,6 +333,11 @@ class AbstractConv2d_gradWeights(BaseAbstractConv2d):
 
     # Update shape/height_width
     def make_node(self, img, topgrad, shape):
+        # Normalize the inputs types
+        gtype = img.type.clone(dtype=topgrad.dtype,
+                               broadcastable=topgrad.broadcastable)
+        topgrad = gtype.filter_variable(topgrad)
+
         if img.type.ndim != 4:
             raise TypeError('img must be 4D tensor')
         if topgrad.type.ndim != 4:
@@ -415,6 +425,11 @@ class AbstractConv2d_gradInputs(BaseAbstractConv2d):
 
     # Update shape/height_width
     def make_node(self, kern, topgrad, shape):
+        # Normalize the inputs types
+        gtype = kern.type.clone(dtype=topgrad.dtype,
+                               broadcastable=topgrad.broadcastable)
+        topgrad = gtype.filter_variable(topgrad)
+
         if kern.type.ndim != 4:
             raise TypeError('kern must be 4D tensor')
         if topgrad.type.ndim != 4: