Some more ShapeError fixes, and moved ShapeError to tensor.basic to avoid import issues

theano/gof/apply_shape.py

@@ -36,7 +36,7 @@ class Apply(gof.Apply):
 
         try:
             oshapes = infer_shape(self, ishapes)
-        except theano.tensor.opt.ShapeError:
+        except theano.tensor.ShapeError:
             return
 
         for o, oshp in zip(outputs, oshapes):

theano/tensor/basic.py

@@ -45,6 +45,11 @@ int_dtypes = map(str, scal.int_types)
 discrete_dtypes = map(str, scal.discrete_types)
 
 
+class ShapeError(Exception):
+    """Raised when the shape cannot be computed."""
+    pass
+
+
 def check_equal_numpy(x, y):
     """
     Returns True iff x and y are equal (checks the dtype and
@@ -3576,14 +3581,14 @@ class Join(Op):
         # that whenever I get a None. Should we just remove gof/apply_shape
         # if it is depricated ??
         if ishapes[1] is None:
-            raise NotImplementedError
+            raise ShapeError()
         n_dim = len(ishapes[1])
         for shape in ishapes[1:]:
             if shape is None:
-                raise NotImplementedError
+                raise ShapeError()
             for shape_i in shape:
                 if shape_i is None:
-                    raise NotImplementedError
+                    raise ShapeError()
             # at this point the inputs have been broadcasted so they should
             # all have the same shape
             assert len(shape) == n_dim

theano/tensor/nnet/conv.py

@@ -575,14 +575,14 @@ class ConvOp(Op):
             try:
                 fmshp = ConvOp.getOutputShape(imshp[1:], kshp, (self.dx,self.dy), self.out_mode)
             except TypeError:
-                raise theano.tensor.opt.ShapeError()
+                raise theano.tensor.ShapeError()
             outshp = (batch_size,fmo) + tuple(fmshp)
             return [outshp]
         else:
             # Haven't implemented this case. imshp and kshp may be symbollic
             # and ConvOp.getOutputShape doesn't handle this. In this case
             # we simply let the default function do its work.
-            raise theano.tensor.opt.ShapeError()
+            raise theano.tensor.ShapeError()
             
 
     def perform(self,node, inp, out):

theano/tensor/opt.py

@@ -27,17 +27,12 @@ from theano import compile  #to register the optimizer built by this file
 from theano.gof.python25 import any, all
 from theano.gof.opt import Optimizer, pre_constant_merge, pre_greedy_local_optimizer
 from theano.gof import toolbox, DestroyHandler
-from basic import get_constant_value
+from basic import get_constant_value, ShapeError
 
 
 # Utilities
 
 
-class ShapeError(Exception):
-    """Raised when the shape cannot be computed."""
-    pass
-
-
 def out2in(*local_opts):
     """WRITEME """
     return opt.TopoOptimizer(opt.LocalOptGroup(*local_opts),
@@ -727,7 +722,7 @@ class ShapeFeature(object):
                     '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.opt.ShapeError '
+                    'supported, and one should now use tensor.ShapeError '
                     'instead. The original exception message is: %s' % e)
         except Exception, e:
             _logger.error('Failed to infer_shape from Op %s (i_shapes=%s): %s %s'% (node.op,