fix test in FAST_COMPILE mode.

theano/tensor/tests/test_opt_uncanonicalize.py

@@ -2,6 +2,7 @@ import unittest
 
 import numpy
 
+import theano
 from theano import function, config
 from theano import scalar
 import theano.tensor as tensor
@@ -13,15 +14,17 @@ from theano.tests import unittest_tools as utt
 class T_max_and_argmax(unittest.TestCase):
     def test_optimization(self):
         #If we use only the max output, we should replace this op with a faster one.
+        mode = theano.compile.mode.get_default_mode().including('canonicalize','fast_run')
+
         data = numpy.asarray(numpy.random.rand(2,3),dtype=config.floatX)
         n = tensor.matrix()
 
-        f = function([n], tensor.max_and_argmax(n,0)[0])
+        f = function([n], tensor.max_and_argmax(n,0)[0], mode=mode)
         topo = f.maker.env.toposort()
         assert len(topo)==1
         assert isinstance(topo[0].op, CAReduce)
 
-        f = function([n], tensor.max_and_argmax(n,0))
+        f = function([n], tensor.max_and_argmax(n,0), mode=mode)
         topo = f.maker.env.toposort()
         assert len(topo)==1
         assert isinstance(topo[0].op, tensor.MaxAndArgmax)
@@ -30,19 +33,20 @@ class T_max_and_argmax(unittest.TestCase):
 class T_min_max(unittest.TestCase):
     def setUp(self):
         utt.seed_rng()
+        self.mode = theano.compile.mode.get_default_mode().including('canonicalize','fast_run')
 
     def test_optimization_max(self):
         data = numpy.asarray(numpy.random.rand(2,3),dtype=config.floatX)
         n = tensor.matrix()
 
-        f = function([n],tensor.max(n,0))
+        f = function([n],tensor.max(n,0), mode=self.mode)
         topo = f.maker.env.toposort()
         assert len(topo)==1
         assert isinstance(topo[0].op,CAReduce)
         f(data)
 
 
-        f = function([n],tensor.max(-n,0))
+        f = function([n],tensor.max(-n,0), mode=self.mode)
         topo = f.maker.env.toposort()
         assert len(topo)==2
         assert isinstance(topo[0].op, Elemwise)
@@ -50,7 +54,7 @@ class T_min_max(unittest.TestCase):
         assert isinstance(topo[1].op,CAReduce)
         f(data)
 
-        f = function([n],-tensor.max(n,0))
+        f = function([n],-tensor.max(n,0), mode=self.mode)
         topo = f.maker.env.toposort()
         assert len(topo)==2
         assert isinstance(topo[0].op,CAReduce)
@@ -58,7 +62,7 @@ class T_min_max(unittest.TestCase):
         assert isinstance(topo[1].op.scalar_op, scalar.Neg)
         f(data)
 
-        f = function([n],-tensor.max(-n,0))
+        f = function([n],-tensor.max(-n,0), mode=self.mode)
         topo = f.maker.env.toposort()
         assert len(topo)==1
         assert isinstance(topo[0].op,CAReduce)#min
@@ -68,14 +72,14 @@ class T_min_max(unittest.TestCase):
         data = numpy.asarray(numpy.random.rand(2,3),dtype=config.floatX)
         n = tensor.matrix()
 
-        f = function([n],tensor.min(n,0))
+        f = function([n],tensor.min(n,0), mode=self.mode)
         topo = f.maker.env.toposort()
         assert len(topo)==1
         assert isinstance(topo[0].op,CAReduce)
         f(data)
 
         #test variant with neg to make sure we optimize correctly
-        f = function([n],tensor.min(-n,0))
+        f = function([n],tensor.min(-n,0), mode=self.mode)
         topo = f.maker.env.toposort()
         assert len(topo)==2
         assert isinstance(topo[0].op,CAReduce)#max
@@ -83,7 +87,7 @@ class T_min_max(unittest.TestCase):
         assert isinstance(topo[1].op.scalar_op, scalar.Neg)
         f(data)
 
-        f = function([n],-tensor.min(n,0))
+        f = function([n],-tensor.min(n,0), mode=self.mode)
         topo = f.maker.env.toposort()
         assert len(topo)==2
         assert isinstance(topo[0].op, Elemwise)
@@ -91,7 +95,7 @@ class T_min_max(unittest.TestCase):
         assert isinstance(topo[1].op,CAReduce)#max
         f(data)
 
-        f = function([n],-tensor.min(-n,0))
+        f = function([n],-tensor.min(-n,0), mode=self.mode)
         topo = f.maker.env.toposort()
         assert len(topo)==1
         assert isinstance(topo[0].op,CAReduce)#max