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

theano/misc/may_share_memory.py

@@ -10,46 +10,38 @@ from theano.tensor.basic import TensorType
 
 try:
     import scipy.sparse
-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")
-            return False
-        return numpy.may_share_memory(a,b)
-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):
-        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
+except ImportError:
+    #scipy not imported, their can be only ndarray and cudandarray
+    def _is_sparse(a):
+        return False
+
+import theano.sandbox.cuda as cuda
+if cuda.cuda_available:
+    def _is_cuda(a):
+        return isinstance(a, cuda.CudaNdarray)
+else:            
+    def _is_cuda(a):
+        return False
 
-        a_cuda = False
-        b_cuda = False
-        if a.__class__.__name__ == "CudaNdarray":
-            a_cuda = True
-        if b.__class__.__name__ == "CudaNdarray":
-            b_cuda = True
+def may_share_memory(a, b, raise_other_type=True):
+    a_ndarray = isinstance(a, numpy.ndarray)
+    b_ndarray = isinstance(b, numpy.ndarray)
+    a_sparse = _is_sparse(a)
+    b_sparse = _is_sparse(b)
+    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:
-                raise TypeError("may_share_memory support only ndarray and scipy.sparse and CudaNdarray type")
-            return False
+    if not(a_ndarray or a_sparse or a_cuda) or not(b_ndarray or b_sparse or b_cuda):
+        if raise_other_type:
+            raise TypeError("may_share_memory support only ndarray and scipy.sparse and CudaNdarray type")
+        return False
 
-        if a_ndarray and b_ndarray:
-            return TensorType.may_share_memory(a,b)
-        if a_cuda and b_cuda:
-            from theano.sandbox.cuda.type import CudaNdarrayType
-            return CudaNdarrayType.may_share_memory(a,b)
-        if a_cuda or b_cuda:
-            return False
-        return SparseType.may_share_memory(a,b)
+    if a_ndarray and b_ndarray:
+        return TensorType.may_share_memory(a,b)
+    if a_cuda and b_cuda:
+        from theano.sandbox.cuda.type import CudaNdarrayType
+        return CudaNdarrayType.may_share_memory(a,b)
+    if a_cuda or b_cuda:
+        return False
+    return SparseType.may_share_memory(a,b)