Merge pull request #6084 from nouiz/tests

theano/gof/cmodule.py

@@ -447,6 +447,10 @@ def get_safe_part(key):
 
     # Find the md5 hash part.
     c_link_key = key[1]
+    # In case in the future, we don't have an md5 part and we have
+    # such stuff in the cache.  In that case, we can set None, and the
+    # rest of the cache mechanism will just skip that key.
+    md5 = None
     for key_element in c_link_key[1:]:
         if (isinstance(key_element, string_types) and
                 key_element.startswith('md5:')):
@@ -743,7 +747,11 @@ class ModuleCache(object):
         time_now = time.time()
         # Go through directories in alphabetical order to ensure consistent
         # behavior.
+        try:
             subdirs = sorted(os.listdir(self.dirname))
+        except OSError:
+            # This can happen if the dir don't exist.
+            subdirs = []
         files, root = None, None  # To make sure the "del" below works
         for subdirs_elem in subdirs:
             # Never clean/remove lock_dir

theano/gof/compiledir.py

@@ -77,7 +77,7 @@ def cleanup():
                     if len(keydata.keys) == 0:
                         shutil.rmtree(os.path.join(compiledir, directory))
 
-                except EOFError:
+                except (EOFError, AttributeError):
                     _logger.error(
                         "Could not read key file '%s'. To complete "
                         "the clean-up, please remove manually "
@@ -159,6 +159,10 @@ def print_compiledir_content():
                 nb_keys[len(keydata.keys)] += 1
             except IOError:
                 pass
+            except AttributeError:
+                    _logger.error(
+                        "Could not read key file '%s'.",
+                        filename)
 
     print_title("Theano cache: %s" % compiledir, overline='=', underline='=')
     print()

theano/gof/type.py

@@ -803,7 +803,8 @@ if (py_%(name)s == NULL) { %(freefunc)s(%(name)s); }
             self.header_dirs = ()
             self.libraries = ()
             self.lib_dirs = ()
-            self.extra_support_code = ""
+        if not hasattr(self, 'version'):
+            self.version = None
 
 
 class CDataTypeConstant(graph.Constant):

theano/tensor/nnet/tests/test_bn.py

@@ -147,7 +147,7 @@ def test_batch_normalization_train():
     utt.seed_rng()
 
     for axes in ('per-activation', 'spatial', (1, 2, 3, 4)):
-        for vartype in (T.tensor5, T.tensor4, T.tensor3, T.matrix, T.vector):
+        for vartype in (T.tensor5, T.tensor3, T.vector):
             x, scale, bias, running_mean, running_var = (vartype(n)
                                                          for n in ('x', 'scale', 'bias',
                                                                    'running_mean',
@@ -338,7 +338,7 @@ def test_batch_normalization_train_broadcast():
 
 def test_batch_normalization_test():
     for axes in ('per-activation', 'spatial', (1, 2, 3, 4)):
-        for vartype in (T.tensor5, T.tensor4, T.tensor3, T.matrix, T.vector):
+        for vartype in (T.tensor5, T.tensor3, T.vector):
             x, scale, bias, mean, var = (vartype(n)
                                          for n in ('x', 'scale', 'bias', 'mean', 'var'))
             ndim = x.ndim

theano/tensor/tests/test_extra_ops.py

@@ -380,7 +380,7 @@ class TestRepeatOp(utt.InferShapeTester):
             self.numpy_unsupported_dtypes = ('uint32', 'int64', 'uint64')
 
     def test_repeatOp(self):
-        for ndim in range(3):
+        for ndim in [1, 3]:
             x = T.TensorType(config.floatX, [False] * ndim)()
             a = np.random.random((10, ) * ndim).astype(config.floatX)
 
@@ -438,13 +438,13 @@ class TestRepeatOp(utt.InferShapeTester):
 
     @attr('slow')
     def test_infer_shape(self):
-        for ndim in range(4):
+        for ndim in [1, 3]:
             x = T.TensorType(config.floatX, [False] * ndim)()
-            shp = (np.arange(ndim) + 1) * 5
+            shp = (np.arange(ndim) + 1) * 3
             a = np.random.random(shp).astype(config.floatX)
 
             for axis in self._possible_axis(ndim):
-                for dtype in tensor.integer_dtypes:
+                for dtype in ["int8", "uint8", "uint64"]:
                     r_var = T.scalar(dtype=dtype)
                     r = np.asarray(3, dtype=dtype)
                     if dtype in self.numpy_unsupported_dtypes: