Added some theano._asarray casts to perform()s in the sparse module.

theano/sparse/basic.py

@@ -286,9 +286,11 @@ class CSMProperties(gof.Op):
 
     def perform(self, node, (csm,), out):
         if self.kmap is None:
-          out[0][0] = csm.data
+            out[0][0] = csm.data
         else:
-          out[0][0] = csm.data[self.kmap]
+            out[0][0] = csm.data[self.kmap]
+        if str(csm.data.dtype) == 'int32':
+            out[0][0] = theano._asarray(out[0][0], dtype='int32')
         #backport
         #out[0][0] = csm.data if self.kmap is None else csm.data[self.kmap]
         out[1][0] = theano._asarray(csm.indices, dtype='int32')
@@ -562,7 +564,6 @@ class AddSS(gof.op.Op):
         if x.type.dtype != y.type.dtype:
             raise NotImplementedError()
         if x.type.format != y.type.format:
-            print x.type.format, y.type.format
             raise NotImplementedError()
         return gof.Apply(self,
                          [x, y],
@@ -815,7 +816,7 @@ class StructuredDotCSC(gof.Op):
                 (a_nrows, b.shape[0]),
                 copy = False)
         #out[0] = a.dot(b)
-        out[0] = a * b
+        out[0] = theano._asarray(a * b, dtype=node.outputs[0].type.dtype)
         assert _is_dense(out[0]) # scipy 0.7 automatically converts to dense
 
     def c_code(self, node, name, (a_val, a_ind, a_ptr, a_nrows, b), (z,), sub):