made tests use DualLinker

_test_tensor.py

@@ -9,6 +9,16 @@ import gof, gof.graph
 from gof.python25 import any
 
 
+def _numpy_checker(x, y):
+    """
+    Checks if x.data and y.data have the same contents.
+    Used in DualLinker to compare C version with Python version.
+    """
+    if (x.data != y.data).any():
+        raise Exception("Output mismatch.", {'performlinker': x.data, 'clinker': y.data})
+
+
+
 #TODO: consider moving this function / functionality to gradient.py
 #      rationale: it's tricky, and necessary everytime you want to verify
 #      gradient numerically
@@ -82,6 +92,11 @@ def check_eq2_c(self, inputs, output, args_in, arg_out):
     val = fn(*args_in)
     self.failUnless( numpy.all(val == arg_out), (val, arg_out))
 
+def check_eq2_both(self, inputs, output, args_in, arg_out):
+    fn = Function(inputs, [output], linker_cls = lambda env: gof.DualLinker(env, _numpy_checker))
+    val = fn(*args_in)
+    self.failUnless( numpy.all(val == arg_out), (val, arg_out))
+
 class T_argmax(unittest.TestCase):
     def setUp(self):
         numpy.random.seed(123784)
@@ -408,24 +423,24 @@ class T_mul(unittest.TestCase):
     def test_elemwise(self):
         a = astensor(0.0)
         b = astensor(0.0)
-        check_eq2(self, [a,b], mul_elemwise(a,b), [3.0, 4.0], 12.0)
-        check_eq2(self, [a,b], mul_elemwise(b,a), [-1.0,2.0], -2.0)
+        check_eq2_both(self, [a,b], mul_elemwise(a,b), [3.0, 4.0], 12.0)
+        check_eq2_both(self, [a,b], mul_elemwise(b,a), [-1.0,2.0], -2.0)
         self.failUnless(isinstance(mul(a,b).owner, Scale))
 
         a = astensor(numpy.ones(2))
         b = astensor(numpy.ones(2))
         aa = numpy.asarray([-0.5, 4.0])
         bb = numpy.asarray([-0.5, 2.0])
-        check_eq2(self, [a,b], mul_elemwise(a,b), [aa,bb], numpy.asarray([0.25, 8.0]))
-        check_eq2(self, [a,b], mul_elemwise(a,b), [bb,aa], numpy.asarray([0.25, 8.0]))
+        check_eq2_both(self, [a,b], mul_elemwise(a,b), [aa,bb], numpy.asarray([0.25, 8.0]))
+        check_eq2_both(self, [a,b], mul_elemwise(a,b), [bb,aa], numpy.asarray([0.25, 8.0]))
         self.failUnless(isinstance(mul(a,b).owner, MulElemwise))
 
     def test_scalar(self):
         r = numpy.random.rand(2,3)
         a = astensor(r)
         b = astensor(2.0)
-        check_eq2(self, [a,b], scale(a,b), [r, 2.0], r*2.0)
-        check_eq2(self, [a,b], scale(a,b), [r, 4.0], r*4.0)
+        check_eq2_both(self, [a,b], scale(a,b), [r, 2.0], r*2.0)
+        check_eq2_both(self, [a,b], scale(a,b), [r, 4.0], r*4.0)
         self.failUnless(b.data == 2.0)
 
     def test_operator(self):
@@ -443,10 +458,16 @@ class T_mul(unittest.TestCase):
         try:
             check_eq2(self, [a,b], MulElemwise(a,b).out,
                       [numpy.ones(3), numpy.ones(4)], 1.0)
+            self.fail()
         except ValueError, e:
             self.failUnless(e[0] is tensor._assert_same_shapes.E_shape)
-            return
+        
+        try:
+            check_eq2_c(self, [a,b], MulElemwise(a,b).out,
+                        [numpy.ones(3), numpy.ones(4)], 1.0)
             self.fail()
+        except ValueError, e:
+            pass
 
 class T_div(unittest.TestCase):
     def setUp(self):