Move local_abstractconv_cudnn_graph function after spatial transformer implementation

theano/gpuarray/dnn.py

@@ -2738,101 +2738,6 @@ def dnn_batch_normalization_test(inputs, gamma, beta, mean, var,
     return result
 
 
-def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
-    if (not isinstance(op, (AbstractConv2d,
-                            AbstractConv2d_gradWeights,
-                            AbstractConv2d_gradInputs))):
-        return
-
-    if version(raises=False) < 6000 and op.filter_dilation != (1, 1):
-        return None
-
-    inp1 = inputs[0]
-    inp2 = inputs[1]
-
-    if not dnn_available(inp1.type.context_name):
-        return
-
-    if op.filter_flip:
-        conv_mode = 'conv'
-    else:
-        conv_mode = 'cross'
-
-    if isinstance(op, AbstractConv2d):
-        rval = dnn_conv(inp1, inp2,
-                        border_mode=op.border_mode,
-                        subsample=op.subsample,
-                        dilation=op.filter_dilation,
-                        direction_hint='forward!',
-                        conv_mode=conv_mode,
-                        num_groups=op.num_groups)
-    elif isinstance(op, AbstractConv2d_gradWeights):
-        shape = (inp2.shape[1], inp1.shape[1],
-                 inputs[2][0], inputs[2][1])
-        rval = dnn_gradweight(inp1, inp2, shape,
-                              border_mode=op.border_mode,
-                              subsample=op.subsample,
-                              dilation=op.filter_dilation,
-                              conv_mode=conv_mode,
-                              num_groups=op.num_groups)
-    elif isinstance(op, AbstractConv2d_gradInputs):
-        shape = (inp2.shape[0], inp1.shape[1],
-                 inputs[2][0], inputs[2][1])
-        rval = dnn_gradinput(inp1, inp2, shape,
-                             border_mode=op.border_mode,
-                             subsample=op.subsample,
-                             dilation=op.filter_dilation,
-                             conv_mode=conv_mode,
-                             num_groups=op.num_groups)
-    return [rval]
-
-
-def local_abstractconv3d_cudnn_graph(op, context_name, inputs, outputs):
-    if (not isinstance(op, (AbstractConv3d,
-                            AbstractConv3d_gradWeights,
-                            AbstractConv3d_gradInputs))):
-        return
-
-    if version(raises=False) < 6000 and op.filter_dilation != (1, 1, 1):
-        return None
-
-    inp1 = inputs[0]
-    inp2 = inputs[1]
-
-    if not dnn_available(inp1.type.context_name):
-        return
-
-    if op.filter_flip:
-        conv_mode = 'conv'
-    else:
-        conv_mode = 'cross'
-
-    if isinstance(op, AbstractConv3d):
-        rval = dnn_conv3d(inp1, inp2,
-                          border_mode=op.border_mode,
-                          subsample=op.subsample,
-                          dilation=op.filter_dilation,
-                          direction_hint='forward!',
-                          conv_mode=conv_mode)
-    elif isinstance(op, AbstractConv3d_gradWeights):
-        shape = (inp2.shape[1], inp1.shape[1],
-                 inputs[2][0], inputs[2][1], inputs[2][2])
-        rval = dnn_gradweight3d(inp1, inp2, shape,
-                                border_mode=op.border_mode,
-                                subsample=op.subsample,
-                                dilation=op.filter_dilation,
-                                conv_mode=conv_mode)
-    elif isinstance(op, AbstractConv3d_gradInputs):
-        shape = (inp2.shape[0], inp1.shape[1],
-                 inputs[2][0], inputs[2][1], inputs[2][2])
-        rval = dnn_gradinput3d(inp1, inp2, shape,
-                               border_mode=op.border_mode,
-                               subsample=op.subsample,
-                               dilation=op.filter_dilation,
-                               conv_mode=conv_mode)
-    return [rval]
-
-
 class GpuDnnTransformerDesc(COp):
     """
     This Op builds a spatial transformer descriptor for use in spatial transformer network
@@ -3116,6 +3021,101 @@ def dnn_spatialtf(img, theta, scale_width=1, scale_height=1, precision=theano.co
     return sampler
 
 
+def local_abstractconv_cudnn_graph(op, context_name, inputs, outputs):
+    if (not isinstance(op, (AbstractConv2d,
+                            AbstractConv2d_gradWeights,
+                            AbstractConv2d_gradInputs))):
+        return
+
+    if version(raises=False) < 6000 and op.filter_dilation != (1, 1):
+        return None
+
+    inp1 = inputs[0]
+    inp2 = inputs[1]
+
+    if not dnn_available(inp1.type.context_name):
+        return
+
+    if op.filter_flip:
+        conv_mode = 'conv'
+    else:
+        conv_mode = 'cross'
+
+    if isinstance(op, AbstractConv2d):
+        rval = dnn_conv(inp1, inp2,
+                        border_mode=op.border_mode,
+                        subsample=op.subsample,
+                        dilation=op.filter_dilation,
+                        direction_hint='forward!',
+                        conv_mode=conv_mode,
+                        num_groups=op.num_groups)
+    elif isinstance(op, AbstractConv2d_gradWeights):
+        shape = (inp2.shape[1], inp1.shape[1],
+                 inputs[2][0], inputs[2][1])
+        rval = dnn_gradweight(inp1, inp2, shape,
+                              border_mode=op.border_mode,
+                              subsample=op.subsample,
+                              dilation=op.filter_dilation,
+                              conv_mode=conv_mode,
+                              num_groups=op.num_groups)
+    elif isinstance(op, AbstractConv2d_gradInputs):
+        shape = (inp2.shape[0], inp1.shape[1],
+                 inputs[2][0], inputs[2][1])
+        rval = dnn_gradinput(inp1, inp2, shape,
+                             border_mode=op.border_mode,
+                             subsample=op.subsample,
+                             dilation=op.filter_dilation,
+                             conv_mode=conv_mode,
+                             num_groups=op.num_groups)
+    return [rval]
+
+
+def local_abstractconv3d_cudnn_graph(op, context_name, inputs, outputs):
+    if (not isinstance(op, (AbstractConv3d,
+                            AbstractConv3d_gradWeights,
+                            AbstractConv3d_gradInputs))):
+        return
+
+    if version(raises=False) < 6000 and op.filter_dilation != (1, 1, 1):
+        return None
+
+    inp1 = inputs[0]
+    inp2 = inputs[1]
+
+    if not dnn_available(inp1.type.context_name):
+        return
+
+    if op.filter_flip:
+        conv_mode = 'conv'
+    else:
+        conv_mode = 'cross'
+
+    if isinstance(op, AbstractConv3d):
+        rval = dnn_conv3d(inp1, inp2,
+                          border_mode=op.border_mode,
+                          subsample=op.subsample,
+                          dilation=op.filter_dilation,
+                          direction_hint='forward!',
+                          conv_mode=conv_mode)
+    elif isinstance(op, AbstractConv3d_gradWeights):
+        shape = (inp2.shape[1], inp1.shape[1],
+                 inputs[2][0], inputs[2][1], inputs[2][2])
+        rval = dnn_gradweight3d(inp1, inp2, shape,
+                                border_mode=op.border_mode,
+                                subsample=op.subsample,
+                                dilation=op.filter_dilation,
+                                conv_mode=conv_mode)
+    elif isinstance(op, AbstractConv3d_gradInputs):
+        shape = (inp2.shape[0], inp1.shape[1],
+                 inputs[2][0], inputs[2][1], inputs[2][2])
+        rval = dnn_gradinput3d(inp1, inp2, shape,
+                               border_mode=op.border_mode,
+                               subsample=op.subsample,
+                               dilation=op.filter_dilation,
+                               conv_mode=conv_mode)
+    return [rval]
+
+
 @local_optimizer([AbstractConv2d, AbstractConv3d])
 def local_abstractconv_cudnn(node):
     ctx = infer_context_name(*node.inputs)