test_pfunc.py 文件源码

def test_sparse_input_aliasing_affecting_inplace_operations(self):
        ##
        # Note this test will never fail because I am not aware of any
        # inplace op on sparse variables
        try:
            import scipy.sparse as sp
        except ImportError:
            # The variable enable_sparse will be used to disable the test file.
            pass

        from theano.sparse import enable_sparse
        if not enable_sparse:
            raise SkipTest('Optional package sparse disabled')

        from theano import sparse

        # Note: to trigger this bug with theano rev 4586:2bc6fc7f218b,
        #        you need to make in inputs mutable (so that inplace
        #        operations are used) and to break the elemwise composition
        #        with some non-elemwise op (here dot)

        x = sparse.SparseType('csc', dtype='float64')()
        y = sparse.SparseType('csc', dtype='float64')()
        f = theano.function([theano.In(x, mutable=True),
                             theano.In(y, mutable=True)],
                            (x + y) + (x + y))
        # Test 1. If the same variable is given twice

        # Compute bogus values
        m = sp.csc_matrix(numpy.asarray(
            [[1, 0, 0, 0, 0],
             [0, 1, 0, 0, 0],
             [0, 0, 1, 0, 0],
             [0, 0, 0, 1, 0],
             [0, 0, 0, 0, 1]], dtype='float64'))
        bogus_vals = f(m, m)
        # Since we used inplace operation v and m may be corrupted
        # so we need to recreate them

        m = sp.csc_matrix(numpy.asarray(
            [[1, 0, 0, 0, 0],
             [0, 1, 0, 0, 0],
             [0, 0, 1, 0, 0],
             [0, 0, 0, 1, 0],
             [0, 0, 0, 0, 1]], dtype='float64'))
        m_copy = m.copy()
        vals = f(m, m_copy)

        assert numpy.allclose(vals.todense(), bogus_vals.todense())