minor modifs

theano/tensor/opt.py

@@ -3296,40 +3296,21 @@ def local_useless_switch(node):
         # Elemwise{switch}(le(shape_i{id}(X), 0), 0, shape_i{id}(X)) -> shape_i{id}(X)
         left = node.inputs[1]
         right = node.inputs[2]
-
-        if (cond.owner and
-            isinstance(cond.owner.op, T.Elemwise) and
-            isinstance(cond.owner.op.scalar_op, scalar.LE) and
-            cond.owner.inputs[0].owner and
-            isinstance(cond.owner.inputs[0].owner.op, Shape_i) and
-            T.extract_constant(cond.owner.inputs[1]) == 0 and
+        cond_var = node.inputs[0]
+        if (cond_var.owner and
+            isinstance(cond_var.owner.op, T.Elemwise) and
+            isinstance(cond_var.owner.op.scalar_op, scalar.LE) and
+            cond_var.owner.inputs[0].owner and
+            isinstance(cond_var.owner.inputs[0].owner.op, Shape_i) and
+            T.extract_constant(cond_var.owner.inputs[1]) == 0 and
             T.extract_constant(left) == 0 and
-            right is cond.owner.inputs[0]):
+            right is cond_var.owner.inputs[0]):
             assert right.type == node.outputs[0].type
             return [right]
         return False
     return False
 
 
-#@register_canonicalize
-#@register_specialize
-@gof.local_optimizer([Shape_i])
-def local_shape_i_infered(node):
-    if not isinstance(node.op, Shape_i):
-        return
-    if not hasattr(node, 'fgraph'):
-        return
-    if not hasattr(node.fgraph, 'shape_feature'):
-        return
-    try:
-        shp = node.fgraph.shape_feature.shape_of[node.inputs[0]][node.op.i]
-        c = get_scalar_constant_value(shp)
-        import pdb;pdb.set_trace()
-        return [T.constant(c, dtype=node.outputs[0].dtype)]
-    except NotScalarConstantError:
-        pass
-
-
 @register_canonicalize
 @gof.local_optimizer([T.mul])
 def local_mul_switch_sink(node):
@@ -4233,9 +4214,10 @@ def local_elemwise_sub_zeros(node):
 def local_useless_elemwise_comparison(node):
     """...
 
-    :note: Those case appear in the graph generated around scan.  This
-           don't remove much computation, but make the graph easier to
-           read.
+    :note: These cases appear in the graph generated by scan.
+           These optimizations will not reduce computation,
+           but will make the graph easier to read.
+    
     # Comparing to itself is constant
     Elemwise[{LT,GT}](X, X) -> Elemwise[zeros](X)
     Elemwise[{LE,GE}](X, X) -> Elemwise[ones](X)