cleaned up the printing of errors during testing tensor/test_basic

theano/gof/opt.py

@@ -900,9 +900,8 @@ class EquilibriumOptimizer(NavigatorOptimizer):
                             lopt_change = self.process_node(env, node, lopt)
                             process_count[lopt] += 1 if lopt_change else 0
                             changed |= lopt_change
-            except:
+            finally:
                 self.detach_updater(env, u)
-                raise
             self.detach_updater(env, u)
         if max_use_abort:
             print >> sys.stderr, "WARNING: EquilibriumOptimizer max'ed out"

theano/gof/optdb.py

@@ -120,7 +120,7 @@ class EquilibriumDB(DB):
         return opt.EquilibriumOptimizer(opts, 
                 max_depth=5,
                 max_use_ratio=10,
-                failure_callback=opt.NavigatorOptimizer.warn)
+                failure_callback=opt.NavigatorOptimizer.warn_inplace)
 
 
 class SequenceDB(DB):

theano/tensor/basic.py

@@ -858,7 +858,7 @@ class MaxAndArgmax(Op):
             axis = x.type.ndim - 1
         axis = _as_tensor_variable(axis)
         inputs = [x, axis]
-        broadcastable = [False] * (x.type.ndim - 1)
+        broadcastable = [False] * (x.type.ndim - 1) #TODO: be less conservative
         outputs = [tensor(x.type.dtype, broadcastable), 
                    tensor(axis.type.dtype, broadcastable)]
         return Apply(self, inputs, outputs)

theano/tensor/tests/test_basic.py

@@ -628,18 +628,26 @@ class T_max_and_argmax(unittest.TestCase):
         self.failUnless(numpy.all(i == numpy.argmax(data,0)))
     def test2_invalid(self):
         n = as_tensor_variable(numpy.random.rand(2,3))
+        old_stderr = sys.stderr
+        sys.stderr = StringIO.StringIO()
         try:
             eval_outputs(max_and_argmax(n,3))
+            assert False
         except ValueError, e:
-            return
-        self.fail()
+            pass
+        finally:
+            sys.stderr = old_stderr
     def test2_invalid_neg(self):
         n = as_tensor_variable(numpy.random.rand(2,3))
+        old_stderr = sys.stderr
+        sys.stderr = StringIO.StringIO()
         try:
             eval_outputs(max_and_argmax(n,-3))
+            assert False
         except ValueError, e:
-            return
-        self.fail()
+            pass
+        finally:
+            sys.stderr = old_stderr
     def test2_valid_neg(self):
         n = as_tensor_variable(numpy.random.rand(2,3))
         v,i = eval_outputs(max_and_argmax(n,-1))
@@ -678,13 +686,16 @@ class T_subtensor(unittest.TestCase):
         n = as_tensor_variable(numpy.ones(3))
         t = n[7]
         self.failUnless(isinstance(t.owner.op, Subtensor))
+        old_stderr = sys.stderr
+        sys.stderr = StringIO.StringIO()
         try:
             tval = eval_outputs([t])
+            assert 0
         except Exception, e:
             if e[0] != 'index out of bounds':
                 raise
-            return
-        self.fail()
+        finally:
+            sys.stderr = old_stderr
     def test1_err_subslice(self):
         n = as_tensor_variable(numpy.ones(3))
         try:
@@ -748,20 +759,28 @@ class T_subtensor(unittest.TestCase):
         n = as_tensor_variable(numpy.ones((2,3))*5)
         t = n[0,4]
         self.failUnless(isinstance(t.owner.op, Subtensor))
+        old_stderr = sys.stderr
+        sys.stderr = StringIO.StringIO()
         try:
             tval = eval_outputs([t])
+            assert 0
         except IndexError, e:
-            return
-        self.fail()
+            pass
+        finally:
+            sys.stderr = old_stderr
     def test2_err_bounds1(self):
         n = as_tensor_variable(numpy.ones((2,3))*5)
         t = n[4:5,2]
         self.failUnless(isinstance(t.owner.op, Subtensor))
+        old_stderr = sys.stderr
+        sys.stderr = StringIO.StringIO()
         try:
             tval = eval_outputs([t])
         except Exception, e:
             if e[0] != 'index out of bounds':
                 raise
+        finally:
+            sys.stderr = old_stderr
     def test2_ok_elem(self):
         n = as_tensor_variable(numpy.asarray(range(6)).reshape((2,3)))
         t = n[0,2]
@@ -1364,9 +1383,9 @@ class t_dot(unittest.TestCase):
 
     def not_aligned(self, x, y):
         z = dot(x,y)
-        old_stderr = sys.stderr
         # constant folding will complain to stderr that things are not aligned
         # this is normal, testers are not interested in seeing that output.
+        old_stderr = sys.stderr
         sys.stderr = StringIO.StringIO()
         try:
             tz = eval_outputs([z])