Code refactoring for later change

theano/tensor/opt.py

@@ -921,6 +921,39 @@ class ShapeFeature(object):
     constants?? That would be confusing.
 
     """
+    def get_node_infer_shape(self, node):
+        try:
+            shape_infer = node.op.infer_shape
+        except AttributeError:
+            shape_infer = self.default_infer_shape
+
+        try:
+            o_shapes = shape_infer(node,
+                                   [self.shape_of[r] for r in node.inputs])
+        except ShapeError:
+            o_shapes = self.default_infer_shape(node, [self.shape_of[r] for
+                                                       r in node.inputs])
+        except NotImplementedError as e:
+            raise NotImplementedError(
+                'Code called by infer_shape failed raising a '
+                'NotImplementedError. Raising NotImplementedError to '
+                'indicate that a shape cannot be computed is no longer '
+                'supported, and one should now use tensor.ShapeError '
+                'instead. The original exception message is: %s' % e)
+        except Exception as e:
+            msg = ('Failed to infer_shape from Op %s.\nInput shapes: '
+                   '%s\nException encountered during infer_shape: '
+                   '%s\nException message: %s\nTraceback: %s') % (
+                node.op, [self.shape_of[r] for r in node.inputs],
+                type(e), str(e), traceback.format_exc())
+            if config.on_shape_error == "raise":
+                raise Exception(msg)
+            else:
+                _logger.warning(msg)
+            o_shapes = self.default_infer_shape(
+                node, [self.shape_of[r] for r in node.inputs])
+
+        return o_shapes
 
     def shape_ir(self, i, r):
         """Return symbolic r.shape[i] for tensor variable r, int i."""
@@ -1207,36 +1240,7 @@ class ShapeFeature(object):
             # make sure we have shapes for the inputs
             self.init_r(r)
 
-        try:
-            shape_infer = node.op.infer_shape
-        except AttributeError:
-            shape_infer = self.default_infer_shape
-
-        try:
-            o_shapes = shape_infer(node,
-                                   [self.shape_of[r] for r in node.inputs])
-        except ShapeError:
-            o_shapes = self.default_infer_shape(node, [self.shape_of[r] for
-                                                       r in node.inputs])
-        except NotImplementedError as e:
-            raise NotImplementedError(
-                'Code called by infer_shape failed raising a '
-                'NotImplementedError. Raising NotImplementedError to '
-                'indicate that a shape cannot be computed is no longer '
-                'supported, and one should now use tensor.ShapeError '
-                'instead. The original exception message is: %s' % e)
-        except Exception as e:
-            msg = ('Failed to infer_shape from Op %s.\nInput shapes: '
-                   '%s\nException encountered during infer_shape: '
-                   '%s\nException message: %s\nTraceback: %s') % (
-                node.op, [self.shape_of[r] for r in node.inputs],
-                type(e), str(e), traceback.format_exc())
-            if config.on_shape_error == "raise":
-                raise Exception(msg)
-            else:
-                _logger.warning(msg)
-            o_shapes = self.default_infer_shape(
-                node, [self.shape_of[r] for r in node.inputs])
+        o_shapes = self.get_node_infer_shape(node)
 
         # this is packed information
         # an element of o_shapes is either None or a tuple