Tests for StructuredDot and gradients.

theano/sparse/tests/test_basic.py

@@ -363,6 +363,22 @@ class test_structureddot(unittest.TestCase):
         utt.verify_grad(buildgraph,
                     [spmat.data, mat])
 
+    def test_infer_shape_csr_csc_grad(self):
+        for sparsetype in ('csr', 'csc'):
+            a = SparseType(sparsetype, dtype=config.floatX)()
+            b = SparseType(sparsetype, dtype=config.floatX)()
+            grads = tensor.grad(dense_from_sparse(structured_dot(a, b)).sum(),
+                                [a, b])
+            f = theano.function([a, b], [g.shape for g in grads])
+            topo = f.maker.env.toposort()
+            assert not any(isinstance(t, self.__class__) for t in topo)
+            call = getattr(sp, sparsetype + '_matrix')
+            x = call(random_lil((500, 300), config.floatX, 10))
+            y = call(random_lil((300, 400), config.floatX, 5))
+            out1, out2 = f(x, y)
+            assert numpy.all(out1 == x.shape)
+            assert numpy.all(out2 == y.shape)
+
     def test_upcast(self):
 
         typenames = ('float32', 'int64', 'int8', 'int32',
@@ -553,6 +569,16 @@ class test_structureddot(unittest.TestCase):
                 self.assertFalse(theano_time > overhead_rtol * scipy_time +
                                  overhead_tol)
 
+    def test_infer_shape(self):
+        a = SparseType('csc', dtype=config.floatX)()
+        b = SparseType('csc', dtype=config.floatX)()
+        f = theano.function([a, b], structured_dot(a, b).shape)
+        topo = f.maker.env.toposort()
+        assert not any(isinstance(t, self.__class__) for t in topo)
+        x = sp.csc_matrix((4, 5), dtype=config.floatX)
+        y = sp.csc_matrix((5, 3), dtype=config.floatX)
+        assert numpy.all(f(x, y) == numpy.array((4, 3)))
+
 
 class DotTests(unittest.TestCase):
     def setUp(self):