Added constant input support for GetItem

theano/typed_list/basic.py

@@ -39,7 +39,13 @@ class GetItem(Op):
 
     def make_node(self, x, index):
         assert isinstance(x.type, TypedListType)
-        assert isinstance(index, Variable)
+        if not isinstance(index, Variable):
+            if isinstance(index, slice):
+                index = Constant(SliceType(), index)
+                return Apply(self, [x, index], [x.type()])
+            else:
+                index = T.constant(index, ndim=0)
+                return Apply(self, [x, index], [x.ttype()])
         if isinstance(index.type, SliceType):
             return Apply(self, [x, index], [x.type()])
         elif isinstance(index, T.TensorVariable) and index.ndim == 0:

theano/typed_list/tests/test_basic.py

@@ -70,6 +70,13 @@ class test_get_item(unittest.TestCase):
 
         self.assertTrue(numpy.array_equal(f([x], numpy.asarray(0)), x))
 
+        z = mySymbolicMatricesList[0: 1: 1]
+
+        f = theano.function([mySymbolicMatricesList],
+                            z)
+
+        self.assertTrue(numpy.array_equal(f([x]), [x]))
+
     def test_wrong_input(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
                             theano.config.floatX, (False, False)))()
@@ -78,17 +85,38 @@ class test_get_item(unittest.TestCase):
         self.assertRaises(TypeError, GetItem(), mySymbolicMatricesList,
                           mySymbolicMatrix)
 
+    def test_constant_input(self):
+        mySymbolicMatricesList = TypedListType(T.TensorType(
+                            theano.config.floatX, (False, False)))()
+
+        z = GetItem()(mySymbolicMatricesList, 0)
+
+        f = theano.function([mySymbolicMatricesList],
+                            z)
+
+        x = rand_ranged_matrix(-1000, 1000, [100, 101])
+
+        self.assertTrue(numpy.array_equal(f([x]), x))
+
+        z = GetItem()(mySymbolicMatricesList, slice(0, 1, 1))
+
+        f = theano.function([mySymbolicMatricesList],
+                            z)
+
+        self.assertTrue(numpy.array_equal(f([x]), [x]))
+
 
 class test_append(unittest.TestCase):
 
-    def test_sanity_check(self):
+    def test_inplace(self):
         mySymbolicMatricesList = TypedListType(T.TensorType(
                             theano.config.floatX, (False, False)))()
         myMatrix = T.matrix()
 
         z = Append()(mySymbolicMatricesList, myMatrix)
 
-        f = theano.function([mySymbolicMatricesList, myMatrix], z)
+        f = theano.function([mySymbolicMatricesList, myMatrix], z,
+                            accept_inplace=True)
 
         x = rand_ranged_matrix(-1000, 1000, [100, 101])
 
@@ -99,7 +127,7 @@ class test_append(unittest.TestCase):
 
 class test_extend(unittest.TestCase):
 
-    def test_sanity_check(self):
+    def test_inplace(self):
         mySymbolicMatricesList1 = TypedListType(T.TensorType(
                             theano.config.floatX, (False, False)))()
         mySymbolicMatricesList2 = TypedListType(T.TensorType(
@@ -108,7 +136,7 @@ class test_extend(unittest.TestCase):
         z = Extend()(mySymbolicMatricesList1, mySymbolicMatricesList2)
 
         f = theano.function([mySymbolicMatricesList1, mySymbolicMatricesList2],
-                            z)
+                            z, accept_inplace=True)
 
         x = rand_ranged_matrix(-1000, 1000, [100, 101])