fix codes

theano/tensor/nnet/abstract_conv2d.py

@@ -264,14 +264,12 @@ class AbstractConv2d_gradWeights(BaseAbstractConv2d):
         d_bottom = AbstractConv2d_gradInputs(self.imshp, self.kshp,
                                              self.bsize,
                                              self.border_mode,
-                                             self.subsample)(
-            weights, top, bottom.shape[-2:])
-                                     d_top = AbstractConv2d(self.imshp,
-                                                            self.kshp,
-                                                            self.bsize,
-                                                            self.border_mode,
-                                                            self.subsample)(
-                                         bottom, weights)
+                                             self.subsample)(weights, top, bottom.shape[-2:])
+        d_top = AbstractConv2d(self.imshp,
+                               self.kshp,
+                               self.bsize,
+                               self.border_mode,
+                               self.subsample)(bottom, weights)
         d_height_width = (theano.gradient.DisconnectedType()(),) * 2 if len(inp) == 4 else ()
         return (d_bottom, d_top) + d_height_width
 
@@ -282,7 +280,7 @@ class AbstractConv2d_gradWeights(BaseAbstractConv2d):
             return [[1], [1], [0], [0]]  # no connection to height, width
 
 
-class AbstractConv2d_gradInputs(Conv2d):
+class AbstractConv2d_gradInputs(BaseAbstractConv2d):
     """Gradient wrt. inputs for `AbstractConv2d`.
 
     :note: You will not want to use this directly, but rely on
@@ -326,11 +324,9 @@ class AbstractConv2d_gradInputs(Conv2d):
         d_weights = AbstractConv2d_gradWeights(self.imshp, self.kshp,
                                                self.bsize,
                                                self.border_mode,
-                                               self.subsample)(
-            bottom, top, weights.shape[-2:])
+                                               self.subsample)(bottom, top, weights.shape[-2:])
         d_top = AbstractConv2d(self.imshp, self.filter_shape, self.bsize,
-                               self.border_mode, self.subsample)(
-            bottom, weights)
+                               self.border_mode, self.subsample)(bottom, weights)
         d_height_width = (theano.gradient.DisconnectedType()(),) * 2 if len(inp) == 4 else ()
         return (d_weights, d_top) + d_height_width
 
@@ -447,10 +443,10 @@ def local_conv2d_cudnn(node):
                         subsample=node.op.subsample,
                         direction_hint='bprop inputs')
         return rval
-register_specialize_device()(local_conv2d_cudnn)
+register_specialize_device(local_conv2d_cudnn)
 
 
-@local_optimizer(AbstractConv2d)
+@local_optimizer([AbstractConv2d])
 def local_conv2d_corrmm(convop, inputs):
 
     img, kern = node.inputs
@@ -505,9 +501,9 @@ def local_conv2d_corrmm(convop, inputs):
             rval = GpuCorrMM_gradInputs('valid', subsample)(
                     gpu_contiguous(kern), gpu_contiguous(img))
         return rval
-register_specialize_device()(local_conv2d_corrmm)
+register_specialize_device(local_conv2d_corrmm)
 
-@local_optimizer(AbstractConv2d_gradWeights)
+@local_optimizer([AbstractConv2d_gradWeights])
 def local_conv2d_gradweight_corrmm(node):
 
     img, topgrad, shape = node.inputs
@@ -518,9 +514,9 @@ def local_conv2d_gradweight_corrmm(node):
     subsample=node.op.subsample)(
     gpu_contiguous(img), gpu_contiguous(topgrad), shape)
     return rval
-register_specialize_device()(local_conv2d_gradweight_corrmm)
+register_specialize_device(local_conv2d_gradweight_corrmm)
 
-@local_optimizer(AbstractConv2d_gradInputs)
+@local_optimizer([AbstractConv2d_gradInputs])
 def local_conv2d_gradinputs_corrmm(node):
 
     kern, topgrad, shape = node.inputs
@@ -531,7 +527,7 @@ def local_conv2d_gradinputs_corrmm(node):
     subsample=node.op.subsample)(
         gpu_contiguous(kern), gpu_contiguous(topgrad), shape)
     return rval
-register_specialize_device()(local_conv2d_gradinputs_corrmm)
+register_specialize_device(local_conv2d_gradinputs_corrmm)
 
 
 

theano/tensor/nnet/tests/test_abstractconv.py

@@ -7,7 +7,7 @@ from theano.tests import unittest_tools as utt
 
 from nose.plugins.skip import SkipTest
 import theano.tensor.nnet.conv as conv_ref
-import theano.tensor.nnet.conv2d as conv
+import theano.tensor.nnet.abstract_conv2d as conv
 from theano.sandbox.cuda import float32_shared_constructor as shared
 
 if theano.config.mode == 'FAST_COMPILE':