quick port of Elemwise, add/sub/mul and scalar_switch

_test_tensor_ops.py

@@ -14,9 +14,12 @@ import sys
 
 
 def inputs():
-    x = modes.build(tensor([[1.0, 2.0], [3.0, 4.0]], 'x'))
-    y = None
-    z = None
+    l1 = [[1.0, 2.0], [3.0, 4.0]]
+    l2 = [[3.0, 4.0], [1.0, 2.0]]
+    l3 = numpy.ones((2, 3))
+    x = modes.build(tensor(l1, 'x'))
+    y = modes.build(tensor(l2, 'y'))
+    z = modes.build(tensor(l3, 'z'))
     return x, y, z
 
 def env(inputs, outputs, validate = True, features = []):
@@ -27,24 +30,34 @@ class _test_TensorOps(unittest.TestCase):
 
     def test_0(self):
         x, y, z = inputs()
-        e = transpose(x)
-        g = env([x], [e])
-        fn, (i, ), (o, ) = gof.cc.CLinker(g).make_thunk()
-        i.data = [[1.0, 2.0], [3.0, 4.0]]
-#        print sys.getrefcount(i.data)
+#        e = mul(add(x, y), 2)
+        e = (x + y) * 2
+        fn, i, o = gof.PerformLinker(env([x, y], [e])).make_thunk(True)
         fn()
-#        print sys.getrefcount(i.data)
-#        print sys.getrefcount(o.data)
-        print o.data
-#        assert res == numpy.asarray(arr)
+        print e
 
-#     def test_1(self):
+#     def test_0(self):
 #         x, y, z = inputs()
-#         e = mul(add(x, y), div(x, y))
-#         g = env([x, y], [e])
-#         fn = gof.cc.CLinker(g).make_function()
-#         assert fn(1.0, 2.0) == 1.5
-#         assert e.data == 1.5
+#         e = transpose(x)
+#         g = env([x], [e])
+#         fn, (i, ), (o, ) = gof.cc.CLinker(g).make_thunk()
+#         i.data = [[1.0, 2.0], [3.0, 4.0]]
+# #        print sys.getrefcount(i.data)
+#         fn()
+# #        print sys.getrefcount(i.data)
+# #        print sys.getrefcount(o.data)
+#         print o.data
+# #        assert res == numpy.asarray(arr)
+
+# #     def test_1(self):
+# #         x, y, z = inputs()
+# #         e = mul(add(x, y), div(x, y))
+# #         g = env([x, y], [e])
+# #         fn = gof.cc.CLinker(g).make_function()
+# #         assert fn(1.0, 2.0) == 1.5
+# #         assert e.data == 1.5
+
+
 
 
 if __name__ == '__main__':

tensor.py

@@ -27,6 +27,7 @@ class Tensor(ResultBase):
         if dtype is None or broadcastable is None:
             if data is None:
                 raise TypeError("Provide non-None data to complete the dtype and broadcastable flags.")
+            data = numpy.asarray(data)
             dtype = data.dtype
             if constant:
                 broadcastable = [1*(x == 1) for x in data.shape]
@@ -35,7 +36,7 @@ class Tensor(ResultBase):
         self.broadcastable = broadcastable
         self.dtype = str(dtype)
         self.constant = constant
-        ResultBase.__init__(self, role = None, data = None, name = name)
+        ResultBase.__init__(self, role = None, data = data, name = name)
 
     def __get_constant(self):
         return self._constant