Move check_and_normalize_axes to Utilies section in basic.py

theano/tensor/basic.py

@@ -18,7 +18,6 @@ from theano.gof import Apply, Constant, Op, Variable
 from theano.gof.type import Generic
 
 from theano.tensor import elemwise
-from theano.tensor.utils import check_and_normalize_axes
 from theano.tensor.var import (AsTensorError, TensorVariable,
                                TensorConstant, TensorConstantSignature,
                                _tensor_py_operators)
@@ -969,6 +968,53 @@ def _pack(x):
         return [x]
 
 
+def check_and_normalize_axes(x, axis):
+    """
+    Check axes, normalize and convert them to a Python list of integers.
+    Return an empty list if argument is None.
+
+    Parameters
+    ----------
+    x: Tensor variable
+    axis = Integer, tuple or list of integers
+
+    Returns
+    -------
+    axis: list of integers
+    """
+    x = as_tensor_variable(x)
+    if axis is None:
+        axis = []
+    elif (isinstance(axis, (integer_types, np.integer)) or
+            (isinstance(axis, np.ndarray) and axis.ndim == 0)):
+                axis = [int(axis)]
+    elif isinstance(axis, (tuple, list, np.ndarray)):
+        axis = [int(i) for i in axis]
+    elif isinstance(axis, Variable):
+        if NoneConst.equals(axis):
+            axis = []
+        elif not isinstance(axis, TensorConstant):
+            raise TypeError("Computation needs a constant axis. Got %s" % axis)
+        else:
+            assert axis.dtype in integer_dtypes
+            if (isinstance(axis.data, (integer_types, np.integer)) or
+                    (isinstance(axis.data, np.ndarray) and axis.data.ndim == 0)):
+                        axis = [int(axis.data)]
+            elif isinstance(axis.data, (list, np.ndarray)):
+                axis = [int(i) for i in axis.data]
+    else:
+        axis = []
+    if len(axis) > 0:
+        for i in range(len(axis)):
+            if axis[i] < 0:
+                axis[i] += x.type.ndim
+            if axis[i] < 0 or axis[i] >= x.type.ndim:
+                raise ValueError("Computation needs a valid axis number for %d-D tensor. Got %d" % (x.type.ndim, axis[i]))
+        axis = list(set(axis))
+        axis.sort()
+    return axis
+
+
 #########################
 # Casting Operations
 #########################

theano/tensor/utils.py

 from __future__ import absolute_import, print_function, division
-from six import integer_types
 import numpy as np
 
 import theano
-from theano import scalar
 from theano.compat import izip
-from theano.gof import Variable
 from theano.gof.utils import hash_from_code
-from theano.tensor.type_other import NoneConst
-
-integer_dtypes = list(map(str, scalar.integer_types))
 
 
 def hash_from_ndarray(data):
@@ -95,50 +89,3 @@ def shape_of_variables(fgraph, input_shapes):
         l[var] = tuple(sym_to_num_dict[sym]
                        for sym in fgraph.shape_feature.shape_of[var])
     return l
-
-
-def check_and_normalize_axes(x, axis):
-    """
-    Check axes, normalize and convert them to a Python list of integers.
-    Return an empty list if argument is None.
-
-    Parameters
-    ----------
-    x: Tensor variable
-    axis = Integer, tuple or list of integers
-
-    Returns
-    -------
-    axis: list of integers
-    """
-    x = tensor.as_tensor_variable(x)
-    if axis is None:
-        axis = []
-    elif (isinstance(axis, (integer_types, np.integer)) or
-            (isinstance(axis, np.ndarray) and axis.ndim == 0)):
-        axis = [int(axis)]
-    elif isinstance(axis, (tuple, list, np.ndarray)):
-        axis = [int(i) for i in axis]
-    elif isinstance(axis, Variable):
-        if NoneConst.equals(axis):
-            axis = []
-        elif not isinstance(axis, tensor.var.TensorConstant):
-            raise TypeError("Computation needs a constant axis. Got %s" % axis)
-        else:
-            assert axis.dtype in integer_dtypes
-            if (isinstance(axis.data, (integer_types, np.integer)) or
-                    (isinstance(axis.data, np.ndarray) and axis.data.ndim == 0)):
-                axis = [int(axis.data)]
-            elif isinstance(axis.data, (list, np.ndarray)):
-                axis = [int(i) for i in axis.data]
-    else:
-        axis = []
-    if len(axis) > 0:
-        for i in range(len(axis)):
-            if axis[i] < 0:
-                axis[i] += x.type.ndim
-            if axis[i] < 0 or axis[i] >= x.type.ndim:
-                raise ValueError("Computation needs a valid axis number for %d-D tensor. Got %d" % (x.type.ndim, axis[i]))
-        axis = list(set(axis))
-        axis.sort()
-    return axis