Replace former stack asserts in nnet/tests/ by the helper function check_stack_trace

theano/tensor/nnet/tests/test_abstract_conv.py

@@ -7,6 +7,7 @@ from nose.tools import assert_raises
 
 import theano
 from theano import tensor
+from theano.gof.opt import check_stack_trace
 from theano.tests import unittest_tools as utt
 from theano.tensor.nnet import corr, abstract_conv as conv
 from theano.tensor.nnet.abstract_conv import get_conv_output_shape
@@ -134,7 +135,7 @@ class BaseTestConv2d(unittest.TestCase):
             assert any([isinstance(n.op, target_op) for n
                         in f.maker.fgraph.toposort()])
 
-        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
+        self.assertTrue(check_stack_trace(f, ops_to_check='all'))
         res_ref = numpy.array(f_ref())
         res = numpy.array(f())
         utt.assert_allclose(res_ref, res)
@@ -177,7 +178,7 @@ class BaseTestConv2d(unittest.TestCase):
                     subsample=subsample,
                     conv_mode=conv_mode)
         f = theano.function([], c, mode=mode)
-        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
+        self.assertTrue(check_stack_trace(f, ops_to_check='all'))
         f_ref = theano.function([], c_ref, mode='FAST_RUN')
 
         if target_op is not None:
@@ -227,7 +228,7 @@ class BaseTestConv2d(unittest.TestCase):
                     border_mode=border_mode, subsample=subsample,
                     conv_mode=conv_mode)
         f = theano.function([], c, mode=mode)
-        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
+        self.assertTrue(check_stack_trace(f, ops_to_check='all'))
         f_ref = theano.function([], c_ref, mode='FAST_RUN')
 
         if target_op is not None:

theano/tensor/nnet/tests/test_conv3d2d.py

@@ -10,6 +10,7 @@ except ImportError:
 from six.moves import xrange
 
 import theano
+from theano.gof.opt import check_stack_trace
 from theano.tensor.nnet.conv3d2d import *
 import theano.tests.unittest_tools as utt
 
@@ -73,10 +74,10 @@ def pyconv3d(signals, filters):
                 r_i += o_i[Tf2:o_i_sh0-Tf2, Hf2:-Hf2, Wf2:-Wf2]
     return rval
 
+
 def check_diagonal_subtensor_view_traces(fn):
-    for apply_node in fn.maker.fgraph.apply_nodes:
-        if isinstance(apply_node.op, (DiagonalSubtensor, IncDiagonalSubtensor)):
-            assert hasattr(apply_node.outputs[0].tag, 'trace')
+    assert check_stack_trace(fn, [DiagonalSubtensor, IncDiagonalSubtensor])
+
 
 def test_conv3d(mode=mode_without_gpu, shared=theano.tensor._shared):
     if ndimage is None:

theano/tensor/nnet/tests/test_nnet.py

@@ -10,6 +10,7 @@ from theano import config
 from theano import tensor as T
 from theano import tensor
 from theano import gof
+from theano.gof.opt import check_stack_trace
 from theano.tests import unittest_tools as utt
 from theano import printing
 from theano.tensor.nnet import (categorical_crossentropy,
@@ -150,8 +151,7 @@ class T_SoftmaxWithBias(utt.InferShapeTester):
         b = theano.shared(numpy.float32(numpy.random.randn()))
         sm = T.nnet.softmax(a + b)
         f = theano.function([], sm)
-        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
-        print('f.maker.fgraph.outputs[0]: {0}'.format(f.maker.fgraph.outputs[0], ))
+        assert check_stack_trace(f, ops_to_check='last')
 
     def test_infer_shape(self):
         admat = matrix()
@@ -256,9 +256,10 @@ class T_LogSoftmax(utt.InferShapeTester):
         sm = tensor.nnet.softmax(x)
         logsm = tensor.log(sm)
         f = theano.function([x], logsm)
-        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
         assert isinstance(f.maker.fgraph.outputs[0].owner.op,
                           theano.tensor.nnet.nnet.LogSoftmax)
+        assert check_stack_trace(
+            f, ops_to_check=theano.tensor.nnet.nnet.LogSoftmax)
 
     def test_local_softmax_grad_optimization_and_big_input(self):
         """Test the Logsoftmax's grad substitution.
@@ -272,7 +273,8 @@ class T_LogSoftmax(utt.InferShapeTester):
         m.check_isfinite = False
         # some inputs that are large to make the gradient explode in the non
         # optimized case
-        a = numpy.exp(10 * numpy.random.rand(5, 10).astype(theano.config.floatX))
+        a = numpy.exp(
+            10 * numpy.random.rand(5, 10).astype(theano.config.floatX))
 
         def myfunc(x):
             sm = tensor.nnet.softmax(x)
@@ -281,7 +283,7 @@ class T_LogSoftmax(utt.InferShapeTester):
         # We set step to 0.1 because for big values we need a big epsilon
         utt.verify_grad(myfunc, [a], eps=0.1, mode=m)
         f = theano.function([], myfunc(a))
-        self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
+        assert check_stack_trace(f, ops_to_check='last')
 
 
 class T_SoftmaxGrad(utt.InferShapeTester):
@@ -659,7 +661,7 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         fgraph = gof.FunctionGraph(
                 [x, one_of_n],
                 [g_x])
-        self.assertTrue(hasattr(fgraph.outputs[0].tag, 'trace'))
+        assert check_stack_trace(fgraph, ops_to_check='last')
 
         # print 'BEFORE'
         # for node in fgraph.toposort():
@@ -755,7 +757,8 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
         for expr in expressions:
             # Verify the optimizer worked on the expressions
             f = theano.function([x, y], expr, mode=mode)
-            self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
+            assert check_stack_trace(
+                f, ops_to_check=crossentropy_softmax_argmax_1hot_with_bias)
             if verbose:
                 theano.printing.debugprint(f)
             try:
@@ -771,7 +774,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
             # Also verify the gradient wrt x
             g = theano.function([x, y], T.grad(expr, x), mode=mode)
-            self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
+            assert check_stack_trace(
+                g, ops_to_check=[crossentropy_softmax_1hot_with_bias_dx,
+                                 softmax_op])
             if verbose:
                 theano.printing.debugprint(g)
             try:
@@ -794,7 +799,8 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
         for expr in bias_expressions:
             f = theano.function([x, b, y], expr, mode=mode)
-            self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
+            assert check_stack_trace(
+                f, ops_to_check=crossentropy_softmax_argmax_1hot_with_bias)
             if verbose:
                 theano.printing.debugprint(f)
             try:
@@ -806,7 +812,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
                 theano.printing.debugprint(f)
                 raise
             g = theano.function([x, b, y], T.grad(expr, x), mode=mode)
-            self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
+            assert check_stack_trace(
+                g, ops_to_check=[crossentropy_softmax_1hot_with_bias_dx,
+                                 softmax_with_bias])
             if verbose:
                 theano.printing.debugprint(g)
             try:
@@ -829,7 +837,8 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
         for expr in mean_expressions:
             f = theano.function([x, y], expr, mode=mode)
-            self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
+            assert check_stack_trace(
+                f, ops_to_check=[crossentropy_softmax_argmax_1hot_with_bias])
             if verbose:
                 theano.printing.debugprint(f)
             try:
@@ -844,7 +853,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
                 raise
 
             g = theano.function([x, y], T.grad(expr, x), mode=mode)
-            self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
+            assert check_stack_trace(
+                g, ops_to_check=[crossentropy_softmax_1hot_with_bias_dx,
+                                 softmax_op])
             if verbose:
                 theano.printing.debugprint(g)
             try:
@@ -868,7 +879,8 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
 
         for expr in mean_bias_expressions:
             f = theano.function([x, b, y], expr, mode=mode)
-            self.assertTrue(hasattr(f.maker.fgraph.outputs[0].tag, 'trace'))
+            assert check_stack_trace(
+                f, ops_to_check=crossentropy_softmax_argmax_1hot_with_bias)
             if verbose:
                 theano.printing.debugprint(f)
             try:
@@ -881,7 +893,9 @@ class T_CrossentropyCategorical1Hot(utt.InferShapeTester):
                 theano.printing.debugprint(f)
                 raise
             g = theano.function([x, b, y], T.grad(expr, x), mode=mode)
-            self.assertTrue(hasattr(g.maker.fgraph.outputs[0].tag, 'trace'))
+            assert check_stack_trace(
+                g, ops_to_check=[crossentropy_softmax_1hot_with_bias_dx,
+                                 softmax_with_bias])
             if verbose:
                 theano.printing.debugprint(g)
             try:
@@ -1295,7 +1309,7 @@ def test_argmax_pushdown():
         fgraph = gof.FunctionGraph(
                 [x],
                 [out])
-        assert hasattr(fgraph.outputs[0].tag, 'trace')
+        assert check_stack_trace(fgraph, ops_to_check='all')
 
         backup = config.warn.argmax_pushdown_bug
         config.warn.argmax_pushdown_bug = False

theano/tensor/nnet/tests/test_opt.py

 from __future__ import absolute_import, print_function, division
 import theano
 from theano import tensor
-from theano.tensor.nnet.blocksparse import sparse_block_dot
+from theano.gof.opt import check_stack_trace
+from theano.tensor.nnet.blocksparse import sparse_block_dot, \
+    sparse_block_gemv_inplace, sparse_block_outer_inplace
 
 
 def test_blocksparse_inplace_gemv_opt():
@@ -14,7 +16,7 @@ def test_blocksparse_inplace_gemv_opt():
     o = sparse_block_dot(W, h, iIdx, b, oIdx)
 
     f = theano.function([W, h, iIdx, b, oIdx], o)
-    assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
+    assert check_stack_trace(f, ops_to_check=sparse_block_gemv_inplace)
 
     if theano.config.mode == "FAST_COMPILE":
         assert not f.maker.fgraph.toposort()[-1].op.inplace
@@ -35,7 +37,7 @@ def test_blocksparse_inplace_outer_opt():
 
     f = theano.function([W, h, iIdx, b, oIdx],
                         [o, tensor.grad(o.sum(), wrt=W)])
-    assert hasattr(f.maker.fgraph.outputs[0].tag, 'trace')
+    assert check_stack_trace(f, ops_to_check=sparse_block_outer_inplace)
 
     if theano.config.mode == "FAST_COMPILE":
         assert not f.maker.fgraph.toposort()[-1].op.inplace