Extract the handle code to a reusable class.

theano/sandbox/cuda/dnn.py

@@ -12,33 +12,10 @@ from theano.sandbox.cuda.basic_ops import (as_cuda_ndarray_variable,
 from theano.sandbox.cuda.blas import GpuConv
 from theano.compat import PY3
 
-
-class GpuDnnConvBase(GpuOp):
-    __props__ = ('border_mode', 'conv_mode')
-
-    def __init__(self, border_mode, conv_mode='conv'):
-        assert border_mode in ('valid', 'full')
-        self.border_mode = border_mode
-        assert conv_mode in ('conv', 'cross')
-        self.conv_mode = conv_mode
-
-    def __setstate__(self, props):
-        self.__dict__.update(props)
-        if not hasattr(self, 'conv_mode'):
-            self.conv_mode = 'conv'
-
-    def make_node(self, img, kern):
-        if img.type.ndim != 4:
-            raise TypeError('img must be 4D tensor')
-        if kern.type.ndim != 4:
-            raise TypeError('kern must be 4D tensor')
-
-        broadcastable = (img.type.broadcastable[0],
-                         kern.type.broadcastable[0],
-                         False, False)
-
-        return Apply(self, [img, kern], [CudaNdarrayType(broadcastable)()])
-
+class DnnBase(GpuOp):
+    """
+    Creates a handle for cudnn and pulls in the cudnn libraries and headers.
+    """
     def c_headers(self):
         return ['cudnn.h', 'cudnn_helper.h']
 
@@ -67,6 +44,33 @@ if ((err = cudnnCreate(&_handle)) != CUDNN_STATUS_SUCCESS) {
 }
 }""" % (error_out,)]
 
+
+class GpuDnnConvBase(DnnBase):
+    __props__ = ('border_mode', 'conv_mode')
+
+    def __init__(self, border_mode, conv_mode='conv'):
+        assert border_mode in ('valid', 'full')
+        self.border_mode = border_mode
+        assert conv_mode in ('conv', 'cross')
+        self.conv_mode = conv_mode
+
+    def __setstate__(self, props):
+        self.__dict__.update(props)
+        if not hasattr(self, 'conv_mode'):
+            self.conv_mode = 'conv'
+
+    def make_node(self, img, kern):
+        if img.type.ndim != 4:
+            raise TypeError('img must be 4D tensor')
+        if kern.type.ndim != 4:
+            raise TypeError('kern must be 4D tensor')
+
+        broadcastable = (img.type.broadcastable[0],
+                         kern.type.broadcastable[0],
+                         False, False)
+
+        return Apply(self, [img, kern], [CudaNdarrayType(broadcastable)()])
+
     def c_support_code_struct(self, node, struct_id):
         types = ['cudnn' + d.capitalize() + 'Descriptor_t'
                  for d in self.descriptors]