Merge pull request #1920 from nouiz/fix_tests

theano/compile/tests/test_profiling.py

@@ -6,23 +6,30 @@ import theano
 import theano.tensor as T
 import StringIO
 
+
 def test_profiling():
 
     old1 = theano.config.profile
     old2 = theano.config.profile_memory
-
+    try:
         theano.config.profile = True
         theano.config.profile_memory = True
 
         x = T.dvector("x")
         y = T.dvector("y")
         z = x + y
-	f = theano.function([x, y], z, profile=True, name="test_profiling")
-	output = f([1, 2, 3, 4],[1, 1, 1, 1])
+        p = theano.ProfileStats(False)
+        if theano.config.mode in ["DebugMode", "DEBUG_MODE"]:
+            m = "FAST_RUN"
+        else:
+            m = None
+        f = theano.function([x, y], z, profile=p, name="test_profiling",
+                            mode=m)
+        output = f([1, 2, 3, 4], [1, 1, 1, 1])
 
         buf = StringIO.StringIO()
         f.profile.summary(buf)
-	
+    finally:
         theano.config.profile = old1
         theano.config.profile_memory = old2
 

theano/sandbox/cuda/basic_ops.py

@@ -3289,7 +3289,7 @@ class GpuContiguous(GpuOp):
                 Py_INCREF(%(z)s);
 
             } else if ((NULL == %(z)s)""" % locals()
-        for i in xrange(len(node.inputs[0].type.broadcastable)):
+        for i in xrange(node.inputs[0].type.ndim):
             str += "\n|| (CudaNdarray_HOST_DIMS(%(input)s)[%(i)s] != CudaNdarray_HOST_DIMS(%(z)s)[%(i)s])" % locals()
         str += """
                 || !CudaNdarray_is_c_contiguous(%(z)s))

theano/tensor/opt.py

@@ -1409,12 +1409,13 @@ class Assert(T.Op):
         check = "\n".join(check)
         return """
         %(check)s
+        Py_XDECREF(%(out)s);
         %(out)s = %(value)s;
         Py_INCREF(%(value)s);
         """ % locals()
 
     def c_code_cache_version(self):
-        return (1, 0)
+        return (1, 1)
 
     def infer_shape(self, node, input_shapes):
         return [input_shapes[0]]

theano/tensor/tests/test_gc.py

@@ -20,30 +20,35 @@ def test_no_reuse():
         return
     assert not 'should not get here'
 
+
 def test_gc_never_pickles_temporaries():
     x = T.dvector()
 
     #print >> sys.stderr, 'BUILDING GRAPH'
-    for i in xrange(2): #TODO: 30 causes like LONG compilation due to MERGE
-        if i :
+    for i in xrange(2):  # TODO: 30 causes like LONG compilation due to MERGE
+        if i:
             r = r + r/10
         else:
             r = x
 
-    optimizer=None
-    optimizer='fast_run'
+    optimizer = None
+    optimizer = 'fast_run'
 
     for f_linker, g_linker in [
-            (theano.PerformLinker(allow_gc = True), theano.PerformLinker(allow_gc=False)), 
-            (theano.OpWiseCLinker(allow_gc = True), theano.OpWiseCLinker(allow_gc=False))]:
+            (theano.PerformLinker(allow_gc=True),
+             theano.PerformLinker(allow_gc=False)),
+            (theano.OpWiseCLinker(allow_gc=True),
+             theano.OpWiseCLinker(allow_gc=False))]:
 
         #f_linker has garbage collection
 
         #g_linker has no garbage collection
 
         #print >> sys.stderr, 'COMPILING'
-        f = theano.function([x], r,mode=theano.Mode(optimizer=optimizer, linker=f_linker))
-        g = theano.function([x], r,mode=theano.Mode(optimizer=optimizer, linker=g_linker))
+        f = theano.function([x], r, mode=theano.Mode(optimizer=optimizer,
+                                                     linker=f_linker))
+        g = theano.function([x], r, mode=theano.Mode(optimizer=optimizer,
+                                                     linker=g_linker))
 
         len_pre_f = len(cPickle.dumps(f))
         len_pre_g = len(cPickle.dumps(g))
@@ -70,7 +75,6 @@ def test_gc_never_pickles_temporaries():
         assert c(f) == c(f)  # some sanity checks on the pickling mechanism
         assert c(g) == c(g)  # some sanity checks on the pickling mechanism
 
-
         # now run the function once to create temporaries within the no-gc
         # linker
         f(numpy.ones(100, dtype='float64'))
@@ -86,28 +90,32 @@ def test_gc_never_pickles_temporaries():
         # allow_gc should leave the function un-changed by calling
         assert len_pre_f == len_post_f
 
-        #assert that g() didn't cause g to grow
-        # because temporaries that weren't collected shouldn't be pickled anyway
+        #assert that g() didn't cause g to grow because temporaries
+        # that weren't collected shouldn't be pickled anyway
         assert len_post_f == len_post_g, (f_linker, len_post_f, len_post_g)
 
 
 def test_merge_opt_runtime():
-    """In the original merge optimization, the following graph took like caused the MERGE
-    optimizer to exhibit really bad performance (quadratic? exponential?)
+    """In the original merge optimization, the following graph took
+    like caused the MERGE optimizer to exhibit really bad performance
+    (quadratic? exponential?)
 
     Ironically, there is actually no merging to do in this graph.
+
     """
     x = T.dvector()
     for i in xrange(50):
-        if i :
+        if i:
             r = r + r/10
         else:
             r = x
     t = time.time()
     f = theano.function([x], r, mode='FAST_COMPILE')
-    # FAST_RUN does in-place optimizer which requires a lot of toposorting, which is actually
-    # pretty slow at the moment.  This test was designed to test MergeOptimizer... so I'm
-    # leaving toposort optimizations for a later date.
+    # FAST_RUN does in-place optimizer which requires a lot of
+    # toposorting, which is actually pretty slow at the moment.  This
+    # test was designed to test MergeOptimizer... so I'm leaving
+    # toposort optimizations for a later date.
     dt = time.time() - t
 
-    assert dt < 5.0 #it should never take longer than 5 seconds to compile this graph
+    # it should never take longer than 5 seconds to compile this graph
+    assert dt < 5.0

theano/tensor/tests/test_opt.py

@@ -502,9 +502,6 @@ class test_canonize(unittest.TestCase):
                 assert(out_dtype == out.dtype)
                 assert numpy.allclose(out, val_inputs[1])
                 topo = f.maker.fgraph.toposort()
-                print "ID TOPO", id, topo, sym_inputs
-                for r, t in f.maker.fgraph.shape_feature.shape_of.items():
-                    print '  ', r, t
                 if topo and not(len(topo)==1 and topo[0].op==deep_copy_op):
                     for node in topo[:-1]:
                         assert isinstance(node.op, Shape_i)
@@ -528,7 +525,6 @@ class test_canonize(unittest.TestCase):
                 out = f(*val_inputs)
                 assert numpy.allclose(out, (1 / val_inputs[1]))
                 topo = f.maker.fgraph.toposort()
-                print topo
                 elem = [t for t in topo if isinstance(t.op, T.Elemwise)]
                 assert len(elem) == nb_elemwise
                 assert isinstance(elem[0].op, (T.Elemwise, ))
@@ -727,7 +723,6 @@ class test_canonize(unittest.TestCase):
                 assert numpy.allclose(out, val_inputs[0] /
                     val_inputs[1] / val_inputs[2])
                 topo = f.maker.fgraph.toposort()
-                print topo
                 assert len(topo) == 2
                 assert isinstance(topo[0].op, (T.Elemwise, ))
                 assert isinstance(topo[0].op.scalar_op,
@@ -746,7 +741,6 @@ class test_canonize(unittest.TestCase):
                 assert numpy.allclose(out, val_inputs[0] / (
                     val_inputs[1] / val_inputs[2]))
                 topo = f.maker.fgraph.toposort()
-                print topo
                 assert len(topo) == 2
                 assert isinstance(topo[0].op, (T.Elemwise, ))
                 assert isinstance(topo[0].op.scalar_op,
@@ -798,13 +792,11 @@ def test_local_merge_abs():
 
     f = theano.function([y, z], (abs(y * z * -2)), mode=mode)
     f(y_val, z_val)
-    theano.printing.debugprint(f)
     assert isinstance(f.maker.fgraph.toposort()[1].op.scalar_op, scal.Abs)
     assert len(f.maker.fgraph.toposort()) == 2
 
     f = theano.function([x, y], abs(x / y), mode=mode)
     f(x_val, y_val)
-    theano.printing.debugprint(f)
     assert isinstance(f.maker.fgraph.toposort()[1].op.scalar_op, scal.Abs)
     assert len(f.maker.fgraph.toposort()) == 2
 
@@ -1511,17 +1503,13 @@ def test_log1p():
     # check trickier cases (and use different dtype)
     y = fmatrix()
     f = function([x, y], T.log(tensor.fill(y, 1) + (x)), mode=m)
-    print f.maker.fgraph.toposort()
     # the first three ops are Shape_i, Shape_i, and Dimshuffle
-    theano.printing.debugprint(f)
     assert [node.op for node in f.maker.fgraph.toposort()][3:] == [
         T.log1p, tensor.alloc]
     f = function([x, y], T.log(0 + (x) + tensor.fill(y, 1.0)), mode=m)
-    theano.printing.debugprint(f)
     assert [node.op for node in f.maker.fgraph.toposort()][3:] == [
         T.log1p, tensor.alloc]
     f = function([x, y], T.log(2 + (x) - tensor.fill(y, 1.0)), mode=m)
-    theano.printing.debugprint(f)
     assert [node.op for node in f.maker.fgraph.toposort()][3:] \
             == [T.log1p, tensor.alloc]
 
@@ -1533,14 +1521,12 @@ def test_log1p():
         # I was never sure if this optimization should work on complex numbers or not.
         z = tensor.zmatrix()
         f = function([z], T.log(1 + (z)), mode=m)
-        theano.printing.debugprint(f)
         assert [node.op for node in f.maker.fgraph.toposort()] == [T.log1p]
 
     if 1:
         # should work for int
         z = tensor.imatrix()
         f = function([z], T.log(1 + (z)), mode=m)
-        theano.printing.debugprint(f)
         assert [node.op for node in f.maker.fgraph.toposort()] == [T.log1p]
 
 
@@ -1559,14 +1545,12 @@ def test_log_add():
     y = dvector()
     f = function([x, y], T.log(T.exp(x) + T.exp(y)), mode=m)
 
-    theano.printing.debugprint(f)
-
-    print f([10000], [10000])  # causes overflow if handled incorrectly
+    f([10000], [10000])  # causes overflow if handled incorrectly
     assert numpy.isfinite(f([10000], [10000]))
     assert numpy.allclose(f([10000], [10000]), 10000 + numpy.log1p(1))
 
     #test that it give the same result when it don't overflow
-    print f([10], [10])  # don't causes overflow
+    f([10], [10])  # don't causes overflow
     assert numpy.allclose(f([10], [10]), 10 + numpy.log1p(1))
 
     # test that it also works with more than two args, (this currently fails)
@@ -1574,10 +1558,9 @@ def test_log_add():
     y = dvector()
     f = function([x, y], T.log(T.exp(x) + T.exp(y) + T.exp(x - y) + T.exp(
         x + y)), mode=m)
-    theano.printing.debugprint(f)
 
     try:
-        print f([10000], [10000])  # causes overflow if handled incorrectly
+        f([10000], [10000])  # causes overflow if handled incorrectly
         assert numpy.allclose(f([10000], [10000]), 20000)
     except AssertionError:
         raise KnownFailureTest(('log(add(exp)) is not stabilized when adding '
@@ -2192,8 +2175,8 @@ class test_local_subtensor_merge(unittest.TestCase):
                                 n_ok += 1
                                 f(x_val, b_v, e_v, s_v, i_v)
 
-            print 'shape: %s' % (x_s,)
-            print '%% OK: %f' % (float(n_ok) * 100 / (n_ok + n_index_err))
+            #print 'shape: %s' % (x_s,)
+            #print '%% OK: %f' % (float(n_ok) * 100 / (n_ok + n_index_err))
 
     @attr('slow')
     def test_none_slice(self):
@@ -2873,41 +2856,30 @@ def test_local_mul_specialize():
 
     f = function([v], v * 1, mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
     nodes == [deep_copy_op]
 
     f = function([v], v * 0, mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
     assert nodes == [Shape_i(0), T.alloc]
 
     f = function([v], v * (-1), mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
     assert nodes == [T.neg]
 
     f = function([v, m], v * 1 * (-m), mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
-    theano.printing.debugprint(f)
     assert nodes == [T.mul]
 
     f = function([v, m], v * 0 * (-m), mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
-    theano.printing.debugprint(f)
     assert nodes == [Shape_i(0), T.alloc]
 
     f = function([v, m], v * (-1) * (-m), mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
-    theano.printing.debugprint(f)
     assert nodes == [T.mul]
 
     f = function([v, m], v * (-1) * m, mode=mode)
     nodes = [node.op for node in f.maker.fgraph.toposort()]
-    print nodes
-    theano.printing.debugprint(f)
     assert nodes == [T.mul]
 
 
@@ -3078,7 +3050,6 @@ class T_useless_elemwise(unittest.TestCase):
         f2 = theano.function([x], T.eq(x, x), mode=self.mode)
         assert numpy.all(f2(vx) == numpy.ones((5, 4)))
         topo2 = f2.maker.fgraph.toposort()
-        print topo2
         #Shape_i{1}(<TensorType(float64, matrix)>), Shape_i{0}(<TensorType(float64, matrix)>), Alloc([[1]], Shape_i{0}.0, Shape_i{1}.0
         assert len(topo2) == 3
         assert isinstance(topo2[-1].op, T.Alloc)
@@ -3097,7 +3068,6 @@ class T_useless_elemwise(unittest.TestCase):
         f2 = theano.function([x], T.neq(x, x), mode=self.mode)
         assert numpy.all(f2(vx) == numpy.zeros((5, 4)))
         topo2 = f2.maker.fgraph.toposort()
-        print topo2
         assert len(topo2) == 3
         assert isinstance(topo2[-1].op, T.Alloc)
 
@@ -3114,7 +3084,6 @@ class T_useless_elemwise(unittest.TestCase):
         f2 = theano.function([x, y], T.mul(x, y), mode=self.mode)
         assert numpy.all(f2(vx, vy) == vx * vy)
         topo2 = f2.maker.fgraph.toposort()
-        print topo2
         assert len(topo2) == 1
         assert isinstance(topo2[0].op, T.Elemwise)
         assert isinstance(topo2[0].op.scalar_op, theano.scalar.Mul)
@@ -3132,7 +3101,6 @@ class T_useless_elemwise(unittest.TestCase):
         f2 = theano.function([x, y], T.add(x, y), mode=self.mode)
         assert numpy.all(f2(vx, vy) == vx + vy)
         topo2 = f2.maker.fgraph.toposort()
-        print topo2
         assert len(topo2) == 1
         assert isinstance(topo2[0].op, T.Elemwise)
         assert isinstance(topo2[0].op.scalar_op, theano.scalar.Add)
@@ -3264,20 +3232,17 @@ class T_local_erf(unittest.TestCase):
         x = T.vector()
 
         f = theano.function([x], 1 + T.erf(x), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [
             T.mul, T.erfc], f.maker.fgraph.toposort()
         f(val)
 
         f = theano.function([x], T.erf(x) + 1, mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [
             T.mul, T.erfc], f.maker.fgraph.toposort()
         f(val)
 
         f = theano.function([x], T.erf(x) + 2, mode=self.mode)
         topo = f.maker.fgraph.toposort()
-        print topo
         assert len(topo) == 2
         assert topo[0].op == T.erf
         assert isinstance(topo[1].op, T.Elemwise)
@@ -3290,26 +3255,22 @@ class T_local_erf(unittest.TestCase):
         x = T.vector()
 
         f = theano.function([x], 1 - T.erf(x), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], 1 + (-T.erf(x)), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], (-T.erf(x)) + 1, mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], 2 - T.erf(x), mode=self.mode)
         topo = f.maker.fgraph.toposort()
-        print topo
         assert len(topo) == 2, f.maker.fgraph.toposort()
         assert topo[0].op == T.erf, f.maker.fgraph.toposort()
         assert isinstance(topo[1].op, T.Elemwise), f.maker.fgraph.toposort()
@@ -3323,23 +3284,19 @@ class T_local_erf(unittest.TestCase):
         x = T.vector()
 
         f = theano.function([x], T.erf(x) - 1, mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc, T.mul]
         print f(val)
 
         f = theano.function([x], T.erf(x) + (-1), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc, T.mul]
         print f(val)
 
         f = theano.function([x], -1 + T.erf(x), mode=self.mode)
-        print f.maker.fgraph.toposort()
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erfc, T.mul]
         print f(val)
 
         f = theano.function([x], T.erf(x) - 2, mode=self.mode)
         topo = f.maker.fgraph.toposort()
-        print topo
         assert len(topo) == 2
         assert topo[0].op == T.erf
         assert isinstance(topo[1].op, T.Elemwise)
@@ -3366,20 +3323,17 @@ class T_local_erfc(unittest.TestCase):
         x = T.vector('x')
 
         f = theano.function([x], 1 - T.erfc(x), mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], (-T.erfc(x)) + 1, mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], 2 - T.erfc(x), mode=self.mode)
         topo = f.maker.fgraph.toposort()
-        theano.printing.debugprint(f)
         assert len(topo) == 2, f.maker.fgraph.toposort()
         assert topo[0].op == T.erfc, f.maker.fgraph.toposort()
         assert isinstance(topo[1].op, T.Elemwise), f.maker.fgraph.toposort()
@@ -3394,19 +3348,16 @@ class T_local_erfc(unittest.TestCase):
         x = T.vector('x')
 
         f = theano.function([x], -1 + T.erfc(-x), mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], T.erfc(-x) - 1, mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
 
         f = theano.function([x], T.erfc(-x) + (-1), mode=self.mode)
-        theano.printing.debugprint(f)
         assert [n.op for n in f.maker.fgraph.toposort()] == [T.erf]\
             , f.maker.fgraph.toposort()
         print f(val)
@@ -3427,13 +3378,11 @@ class T_local_erfc(unittest.TestCase):
         mode_fusion.check_isfinite = False
 
         f = theano.function([x], T.log(T.erfc(x)), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(val)))
 
         f = theano.function([x], T.log(T.erfc(-x)), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 24, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(-val)))
@@ -3470,7 +3419,6 @@ class T_local_erfc(unittest.TestCase):
         mode_fusion.check_isfinite = False
 
         f = theano.function([x], T.grad(T.log(T.erfc(x)).sum(), x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert all(numpy.isfinite(f(val)))
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
@@ -3478,14 +3426,12 @@ class T_local_erfc(unittest.TestCase):
         #test with a different mul constant
         f = theano.function([x], T.mul(T.exp(T.neg(T.sqr(x))), -
             10.12837917) / T.erfc(x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(val)))
 
         #test that we work without the mul
         f = theano.function([x], T.exp(T.neg(T.sqr(x))) / T.erfc(x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(val)))
@@ -3493,14 +3439,12 @@ class T_local_erfc(unittest.TestCase):
         #test that we don't work if x!=y
         f = theano.function([x, y], T.exp(T.neg(T.sqr(x))) / T.erfc(
             y), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 5, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         f(val, val - 3)
 
         #test that we work without the sqr and neg
         f = theano.function([x], T.exp(T.mul(-1, x, x)) / T.erfc(x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 22, len(f.maker.fgraph.apply_nodes)
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
         assert all(numpy.isfinite(f(val)))
@@ -3508,7 +3452,6 @@ class T_local_erfc(unittest.TestCase):
         #test that it work correctly if x is x*2 in the graph.
         f = theano.function([x], T.grad(T.log(T.erfc(2 * x)).sum(),
              x), mode=mode)
-        #theano.printing.debugprint(f)
         assert len(f.maker.fgraph.apply_nodes) == 23, len(f.maker.fgraph.apply_nodes)
         assert numpy.isfinite(f(val)).all()
         assert f.maker.fgraph.outputs[0].dtype == theano.config.floatX
@@ -3587,7 +3530,6 @@ class test_local_remove_switch_const_cond(unittest.TestCase):
 
         z = theano.tensor.switch(1, x, y)
         f = theano.function([x, y], z, mode=self.mode)
-        #theano.printing.debugprint(f)
         assert len([node.op for node in f.maker.fgraph.toposort() if
                     isinstance(node.op, theano.tensor.Elemwise) and
                     not isinstance(node.op.scalar_op,theano.scalar.basic.Cast)]) == 0
@@ -3597,7 +3539,6 @@ class test_local_remove_switch_const_cond(unittest.TestCase):
 
         z = theano.tensor.switch(0, x, y)
         f = theano.function([x, y], z, mode=self.mode)
-        #theano.printing.debugprint(f)
         assert len([node.op for node in f.maker.fgraph.toposort() if
                     isinstance(node.op, theano.tensor.Elemwise)]) == 0
         vx = numpy.array([[1, 2, 3], [4, 5, 6]], dtype='int32')
@@ -3912,9 +3853,7 @@ class T_local_sum_dimshuffle(unittest.TestCase):
                 print i
                 f = theano.function([a, b, c, d], s, mode=self.mode,
                         on_unused_input='ignore')
-                theano.printing.debugprint(f)
                 g = f.maker.fgraph.toposort()
-                #print 'g =', g
                 assert isinstance(g[-1].op.scalar_op,
                                   theano.scalar.basic.TrueDiv)
                 f(a_val, b_val, c_val, d_val)
@@ -4157,8 +4096,6 @@ def test_local_div_to_inv():
     denom_m = denom_s.dimshuffle('x', 'x')
 
     out = num_v / denom_m
-    theano.printing.debugprint(out, print_type=True)
-    print out.broadcastable
     assert numpy.all(out.broadcastable == (True, False))
 
     f = theano.function([num_len_s, denom_s], out)