Added fix suggested by Pascal for the tests when dealing with the exception…

theano/tensor/basic.py

@@ -2319,7 +2319,10 @@ class Subtensor(Op):
 
         idx_list = list(self.idx_list)
         if len(idx_list) > x.type.ndim:
-            raise ValueError(Subtensor.e_invalid%(len(idx_list), x.type.ndim))
+            exception = ValueError(Subtensor.e_invalid%(len(idx_list),
+                                                        x.type.ndim))
+            exception.subtensor_invalid = True
+            raise exception
 
         #infer the broadcasting pattern
         padded = idx_list + [slice(0,sys.maxint,1)] * (x.type.ndim - len(idx_list))
@@ -2598,7 +2601,10 @@ class IncSubtensor(Op):
 
         idx_list = list(self.idx_list)
         if len(idx_list) > x.type.ndim:
-            raise ValueError(Subtensor.e_invalid%(len(idx_list), x.type.ndim))
+            exception = ValueError(Subtensor.e_invalid%(len(idx_list),
+                                                        x.type.ndim))
+            exception.subtensor_invalid = True
+            raise exception
 
         #infer the broadcasting pattern
         padded = idx_list + [slice(0,sys.maxint,1)] * (x.type.ndim - len(idx_list))

theano/tensor/tests/test_basic.py

@@ -647,7 +647,7 @@ TanhInplaceTester = makeBroadcastTester(op = inplace.tanh_inplace,
                                           grad = _grad_broadcast_unary_normal,
                                           inplace = True)
 
-#inplace ops when the input is integer and the output is float* 
+#inplace ops when the input is integer and the output is float*
 # don't have a well defined behavior. We don't test that case.
 _good_broadcast_unary_normal_no_int = _good_broadcast_unary_normal.copy()
 del _good_broadcast_unary_normal_no_int['integers']
@@ -903,7 +903,7 @@ class T_max_and_argmax(unittest.TestCase):
     def test_grad(self):
         data = numpy.random.rand(2,3)
         n = as_tensor_variable(data)
-        
+
         def check_grad_max(data, max_grad_data, axis=None):
             #This work only for axis in [0,None]
             assert axis in [0,None]
@@ -915,7 +915,7 @@ class T_max_and_argmax(unittest.TestCase):
             else:
                 for id,v in enumerate(argmax):
                     z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1
-            
+
             z = z.reshape(data.shape)
             assert numpy.all(max_grad_data == z)
 
@@ -1053,7 +1053,7 @@ class T_argmin_argmax(unittest.TestCase):
     def test_grad_argmin(self):
         data = numpy.random.rand(2,3)
         n = as_tensor_variable(data)
-        
+
         #test grad of argmin
         utt.verify_grad(lambda v: argmin(v), [data])
 
@@ -1072,7 +1072,7 @@ class T_argmin_argmax(unittest.TestCase):
     def test_grad_argmax(self):
         data = numpy.random.rand(2,3)
         n = as_tensor_variable(data)
-        
+
         #test grad of argmax
         utt.verify_grad(lambda v: argmax(v), [data])
 
@@ -1172,7 +1172,7 @@ class T_min_max(unittest.TestCase):
             v = eval_outputs(fct(n,-2))
             self.failUnless(v.shape == (3,))
             self.failUnless(numpy.all(v == nfct(n.value,-2)))
-            
+
             v = eval_outputs(fct(n,-1).shape)
             assert v==(2)
             v = eval_outputs(fct(n,-2).shape)
@@ -1220,7 +1220,7 @@ class T_min_max(unittest.TestCase):
     def test_grad_max(self):
         data = numpy.random.rand(2,3)
         n = as_tensor_variable(data)
-        
+
         def check_grad_max(data, max_grad_data, axis=None):
             #This work only for axis in [0,None]
             assert axis in [0,None]
@@ -1232,7 +1232,7 @@ class T_min_max(unittest.TestCase):
             else:
                 for id,v in enumerate(argmax):
                     z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1
-            
+
             z = z.reshape(data.shape)
             assert numpy.all(max_grad_data == z)
 
@@ -1252,7 +1252,7 @@ class T_min_max(unittest.TestCase):
     def test_grad_min(self):
         data = numpy.random.rand(2,3)
         n = as_tensor_variable(data)
-        
+
         def check_grad_min(data, min_grad_data, axis=None):
             #This work only for axis in [0,None]
             assert axis in [0,None]
@@ -1264,7 +1264,7 @@ class T_min_max(unittest.TestCase):
             else:
                 for id,v in enumerate(argmin):
                     z[v*numpy.prod(data.shape[data.ndim-1:axis:-1])+id]+=1
-            
+
             z = z.reshape(data.shape)
             assert numpy.all(min_grad_data == z)
 
@@ -1304,7 +1304,7 @@ class T_subtensor(unittest.TestCase):
         try:
             t = n[0]
         except ValueError, e:
-            self.failUnless(e[0] is Subtensor.e_invalid)
+            self.failUnless(hasattr(e,'subtensor_invalid'))
             return
         self.fail()
 
@@ -1356,7 +1356,7 @@ class T_subtensor(unittest.TestCase):
         try:
             t = n[0,0]
         except ValueError, e:
-            self.failUnless(e[0] is Subtensor.e_invalid)
+            self.failUnless(hasattr(e,'subtensor_invalid'))
             return
         self.fail()
     def test1_ok_elem(self):
@@ -2561,7 +2561,7 @@ def test_flatten_outdim_invalid():
 # TODO: write test case for Tile Op
 def test_tile():
     print >> sys.stderr, "WARNING: No testcase for Tile"
-    pass 
+    pass
 
 
 class TestARange(unittest.TestCase):
@@ -2724,7 +2724,7 @@ class TestARange(unittest.TestCase):
         f = function([stop], out.shape, mode=mode)
         assert len(f.maker.env.toposort())==2
         #[Elemwise{Cast{int64}}(stop), MakeVector(Elemwise{Cast{int64}}.0)]
-        
+
         assert out.dtype == start.type.dtype
         assert numpy.all(f(5) == len(numpy.arange(0,5)))
         assert numpy.all(f(11) == len(numpy.arange(0,11)))
@@ -2961,7 +2961,7 @@ class test_tensordot(unittest.TestCase):
         self.failUnless(numpy.allclose(numpy.tensordot(aval,bval,axes),
                                        f5(aval,bval)))
         utt.verify_grad(TensorDot(axes), [aval,bval])
-        
+
         axes = (axes[1],axes[0])
         c = tensordot(axes)(btens, atens)
         f6 = inplace_func([btens,atens],c)
@@ -3051,7 +3051,7 @@ class test_tensordot(unittest.TestCase):
 
     def test_tensordot_grad(self):
         #We test it manually as we recreate the op in the make_node
-        
+
         amat = matrix()
         bmat = matrix()
         gzmat = matrix()
@@ -3245,17 +3245,17 @@ class test_broadcast(unittest.TestCase):
         test that the unbroadcast fct don't insert not needed broadcast
         and fuse consecutive Rebroadcast op
         """
-        
+
         x=matrix()
         assert unbroadcast(x,0) is x
         assert unbroadcast(x,1) is x
         assert unbroadcast(x,1,0) is x
         assert unbroadcast(x,0,1) is x
-        
+
         assert addbroadcast(x,0) is not x
         assert addbroadcast(x,1) is not x
         assert addbroadcast(x,1,0).owner.inputs[0] is x
-        
+
         assert unbroadcast(addbroadcast(x,0),0) is x
         assert addbroadcast(unbroadcast(x,0),0) is not x
         x=row()
@@ -3263,15 +3263,15 @@ class test_broadcast(unittest.TestCase):
         assert unbroadcast(x,1) is x
         assert unbroadcast(x,1,0) is not x
         assert unbroadcast(x,0,1) is not x
-        
+
         assert addbroadcast(x,0) is x
         assert addbroadcast(x,1).owner.inputs[0] is x
         assert addbroadcast(x,1,0).owner.inputs[0] is x
         assert addbroadcast(x,0,1).owner.inputs[0] is x
-        
+
         assert unbroadcast(addbroadcast(x,1),1) is x
         assert addbroadcast(unbroadcast(x,1),1) is not x
-        
+
         #the first broadcast is remove the broadcast, so the second
         #should not make one
         assert unbroadcast(unbroadcast(x,0),0).owner.inputs[0] is x
@@ -3281,10 +3281,10 @@ class test_broadcast(unittest.TestCase):
         assert unbroadcast(unbroadcast(x,1),0).owner.inputs[0] is x
         assert addbroadcast(unbroadcast(x,1),0).owner.inputs[0] is x
         assert addbroadcast(unbroadcast(x,0),0) is x
-    
+
 def test_mod():
     """
-    We add this test as not all language and C implementation give the same 
+    We add this test as not all language and C implementation give the same
     signe to the result. This check that the c_code of `Mod` is implemented
     as Python. That is what we want.
     """
@@ -3298,7 +3298,7 @@ def test_mod():
 
 def test_mod_compile():
     """
-    This test generate an Elemwise of Composite as: 
+    This test generate an Elemwise of Composite as:
         Elemwise{Composite{Composite{Composite{Composite{mod,EQ},Switch},mul},add}}
 
     The c_code generated is not compiling as of 30 June 2010. I fix the compilation in the same commit.