added check for cycle_detection flag in test

theano/tensor/nnet/tests/test_nnet.py

@@ -580,8 +580,13 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         theano.compile.mode.optdb.query(
             theano.compile.mode.OPT_FAST_RUN).optimize(fgraph)
 
-        assert (fgraph.outputs[0].owner.op ==
-                crossentropy_softmax_argmax_1hot_with_bias)
+        if theano.config.cycle_detection == 'fast':
+            assert (fgraph.outputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
+        else:
+            assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
+            assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_optimizations_vector(self):
         x = tensor.vector('x')
@@ -594,8 +599,13 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
         theano.compile.mode.optdb.query(
             theano.compile.mode.OPT_FAST_RUN).optimize(fgraph)
-        assert (fgraph.outputs[0].owner.op ==
-                crossentropy_softmax_argmax_1hot_with_bias)
+        if theano.config.cycle_detection == 'fast':
+            assert (fgraph.outputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
+        else:
+            assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
+            assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_optimizations_w_bias(self):
         x = tensor.matrix('x')
@@ -623,9 +633,15 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         #    print node.op
         # print printing.pprint(node.outputs[0])
         # print '===='
-        assert len(fgraph.toposort()) == 1
-        assert (fgraph.outputs[0].owner.op ==
-                crossentropy_softmax_argmax_1hot_with_bias)
+        if theano.config.cycle_detection == 'fast':
+            assert len(fgraph.toposort()) == 1
+            assert (fgraph.outputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
+        else:
+            assert len(fgraph.toposort()) == 2
+            assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
+            assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_optimizations_w_bias2(self):
         x = tensor.matrix('x')
@@ -651,10 +667,15 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         # for node in fgraph.toposort():
         #    print node.op
         # print '===='
-        assert len(fgraph.toposort()) == 2
-
-        assert (fgraph.outputs[0].owner.op ==
-                crossentropy_softmax_argmax_1hot_with_bias)
+        if theano.config.cycle_detection == 'fast':
+            assert len(fgraph.toposort()) == 2
+            assert (fgraph.outputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
+        else:
+            assert len(fgraph.toposort()) == 3
+            assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
+            assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_optimizations_w_bias_vector(self):
         x = tensor.vector('x')
@@ -677,9 +698,15 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         # for node in fgraph.toposort():
         #    print node.op
         # print '===='
-        assert len(fgraph.toposort()) == 2
-        assert (fgraph.outputs[0].owner.op ==
-                crossentropy_softmax_argmax_1hot_with_bias)
+        if theano.config.cycle_detection == 'fast':
+            assert len(fgraph.toposort()) == 2
+            assert (fgraph.outputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
+        else:
+            assert len(fgraph.toposort()) == 3
+            assert str(fgraph.outputs[0].owner.op) == 'OutputGuard'
+            assert (fgraph.outputs[0].owner.inputs[0].owner.op ==
+                    crossentropy_softmax_argmax_1hot_with_bias)
 
     def test_softmax_grad_optimizations(self):
         x = tensor.matrix('x')
@@ -1381,7 +1408,13 @@ def test_argmax_pushdown_bias():
     # for node in fgraph.toposort():
     #    print node.op
     types_to_check = (tensor.DimShuffle, tensor.Elemwise, tensor.Argmax)
-    assert len(fgraph.toposort()) == 3
+
+    if theano.config.cycle_detection == 'fast':
+        assert len(fgraph.toposort()) == 3
+    else:
+        assert len(fgraph.toposort()) == 4
+        assert str(fgraph.toposort()[3].op) == 'OutputGuard'
+
     for i, type in enumerate(types_to_check):
         assert isinstance(fgraph.toposort()[i].op, type)
     assert check_stack_trace(fgraph, ops_to_check=types_to_check)
@@ -1404,7 +1437,11 @@ def test_argmax_pushdown_bias():
     # print 'AFTER'
     # for node in fgraph.toposort():
     #    print node.op
-    assert len(fgraph.toposort()) == 2
+    if theano.config.cycle_detection == 'fast':
+        assert len(fgraph.toposort()) == 2
+    else:
+        assert len(fgraph.toposort()) == 3
+        assert str(fgraph.toposort()[2].op) == 'OutputGuard'
     assert isinstance(fgraph.toposort()[0].op, SoftmaxWithBias)
     assert isinstance(fgraph.toposort()[1].op, tensor.CAReduce)
     assert isinstance(fgraph.toposort()[1].op.scalar_op, theano.scalar.Maximum)