merge

theano/tensor/basic.py

@@ -69,13 +69,18 @@ def __oplist_tag(thing, tag):
     thing.__oplist_tags = tags
 
 
-def as_cuda_or_tensor_variable(x, name = None, ndim=None):
-    """
-    This function do the same as_tensor_variable, but don't transfert the value on the gpu
-    """
-    if hasattr(x, '_as_CudaNdarrayVariable'):
-        return x._as_CudaNdarrayVariable() #TODO: pass name and ndim arguments
-    return as_tensor_variable(x, name, ndim)
+if 0:
+    # this starts to feel like we're enumerating all the types
+    # the one place where this is used we should also allow for sparse
+    # variables
+    # - JB 20100226
+    def as_cuda_or_tensor_variable(x, name = None, ndim=None):
+        """
+        This function do the same as_tensor_variable, but don't transfert the value on the gpu
+        """
+        if hasattr(x, '_as_CudaNdarrayVariable'):
+            return x._as_CudaNdarrayVariable() #TODO: pass name and ndim arguments
+        return as_tensor_variable(x, name, ndim)
     
 def as_tensor_variable(x, name = None, ndim=None):
     """Return `x`, transformed into a `TensorType`

theano/tensor/opt.py

@@ -279,10 +279,11 @@ class Shape_i(T.Op):
     def __str__(self):
         return '%s{%i}'%(self.__class__.__name__, self.i)
     def make_node(self, x):
-        #we use as_cuda_or_tensor_variable as we want this op to work for
-        # TensorVariable AND CudaNdarrayVariable. Otherwise, we force the transfert
-        # of the variable to the cpu.
-        x = T.as_cuda_or_tensor_variable(x)
+        # x could be one of a number of types
+        # the only thing we require is that the variable have a .ndim,
+        # and that the value have a .shape
+        if not isinstance(x, T.Variable):
+            raise TypeError('x must be Variable with ndim attribute', x)
         if x.ndim <= self.i:
             raise TypeError('x has too few dimensions for Shape_i', (x, self.i))
         return T.Apply(self, [x], [T.lscalar()])