correctly detect if CudaNdarray may share memory when scipy is not available.

theano/misc/may_share_memory.py

@@ -10,35 +10,27 @@ from theano.tensor.basic import TensorType
 
 try:
     import scipy.sparse
+    from theano.sparse.basic import _is_sparse, SparseType
 except ImportError:
-    #scipy not imported, their can be only ndarray
-    def may_share_memory(a, b, raise_other_type=True):
-        if not isinstance(a, numpy.ndarray) or not isinstance(b, numpy.ndarray):
-            if raise_other_type:
-                raise TypeError("may_share_memory support only ndarray when scipy is not available")
+    #scipy not imported, their can be only ndarray and cudandarray
+    def _is_sparse(a):
         return False
-        return numpy.may_share_memory(a,b)
+
+import theano.sandbox.cuda as cuda
+if cuda.cuda_available:
+    def _is_cuda(a):
+        return isinstance(a, cuda.CudaNdarray)
 else:            
-    #scipy imported, their can be ndarray and sparse type
-    from theano.sparse.basic import _is_sparse, SparseType
-    def may_share_memory(a, b, raise_other_type=True):
+    def _is_cuda(a):
+        return False
+
+def may_share_memory(a, b, raise_other_type=True):
     a_ndarray = isinstance(a, numpy.ndarray)
     b_ndarray = isinstance(b, numpy.ndarray)
-        try:
     a_sparse = _is_sparse(a)
-        except NotImplementedError:
-            a_sparse = False
-        try:
     b_sparse = _is_sparse(b)
-        except NotImplementedError:
-            b_sparse = False
-
-        a_cuda = False
-        b_cuda = False
-        if a.__class__.__name__ == "CudaNdarray":
-            a_cuda = True
-        if b.__class__.__name__ == "CudaNdarray":
-            b_cuda = True
+    a_cuda = _is_cuda(a)
+    b_cuda = _is_cuda(b)
 
     if not(a_ndarray or a_sparse or a_cuda) or not(b_ndarray or b_sparse or b_cuda):
         if raise_other_type: