Address minor comments.

theano/gpuarray/tests/test_dnn.py

@@ -490,6 +490,9 @@ def test_pooling_opt():
         pool_2d(x, ds=(2, 3), mode='sum',
                 ignore_border=True),
         mode=mode_with_gpu)
+
+    assert any([isinstance(n.op, dnn.GpuDnnPool)
+                for n in f.maker.fgraph.toposort()])
     data = numpy.random.rand(10, 10).astype('float32')
     f(data)
 
@@ -538,7 +541,7 @@ def test_pooling_opt_arbitrary_dimensions():
         for ws in ((2, 2), (3, 3, 3)):
             # create input shape: non-pooling dimensions
             # followed by 2 or 3 pooling dimensions
-            shp = (2,) * n_non_pool_dims + (5,) * len(ws)
+            shp = tuple(range(2, 2 + n_non_pool_dims)) + tuple(range(5, 5 + len(ws)))
             data = numpy.random.normal(0, 1, shp).astype('float32')
             input = gpuarray_shared_constructor(data)
 

theano/sandbox/cuda/opt.py

@@ -1914,7 +1914,7 @@ def _check_constant_args_pool(ndim, ws, stride, pad, node):
 @local_optimizer([pool.Pool])
 def local_gpu_downsample_factor_max(node):
     if (isinstance(node.op, pool.Pool)):
-        assert node.op.__props__ == ('ndim', 'ignore_border', 'mode')
+        assert node.op.__props__ == ('ignore_border', 'mode', 'ndim')
         x, ws, stride, pad = node.inputs
         nd = node.op.ndim if node.op.ndim else (x.ndim - 2)
         ret = _check_constant_args_pool(nd, ws, stride, pad, node)
@@ -1941,7 +1941,7 @@ def local_gpu_downsample_factor_max(node):
 @local_optimizer([pool.MaxPoolGrad])
 def local_gpu_downsample_factor_max_grad(node):
     if (isinstance(node.op, pool.MaxPoolGrad)):
-        assert node.op.__props__ == ('ndim', 'ignore_border', 'mode')
+        assert node.op.__props__ == ('ignore_border', 'mode', 'ndim')
         x, z, gz, ws, stride, pad = node.inputs
         nd = node.op.ndim if node.op.ndim else (x.ndim - 2)
         ret = _check_constant_args_pool(nd, ws, stride, pad, node)
@@ -1972,7 +1972,7 @@ def local_gpu_downsample_factor_max_grad(node):
 @local_optimizer([pool.DownsampleFactorMaxGradGrad])
 def local_gpu_downsample_factor_max_grad_grad(node):
     if isinstance(node.op, pool.DownsampleFactorMaxGradGrad):
-        assert node.op.__props__ == ('ndim', 'ignore_border', 'mode')
+        assert node.op.__props__ == ('ignore_border', 'mode', 'ndim')
         x, z, gx, ws, stride, pad = node.inputs
         nd = node.op.ndim if node.op.ndim else (x.ndim - 2)
         ret = _check_constant_args_pool(nd, ws, stride, pad, node)

theano/sandbox/cuda/tests/test_dnn.py

@@ -599,7 +599,7 @@ def test_pooling_opt_arbitrary_dimensions():
         for ws in ((2, 2), (3, 3, 3)):
             # create input shape: non-pooling dimensions
             # followed by 2 or 3 pooling dimensions
-            shp = (2,) * n_non_pool_dims + (5,) * len(ws)
+            shp = tuple(range(2, 2 + n_non_pool_dims)) + tuple(range(5, 5 + len(ws)))
             data = numpy.random.normal(0, 1, shp).astype('float32')
             input = shared(data)
 

theano/tensor/signal/pool.py

@@ -176,7 +176,7 @@ class Pool(OpenMPOp):
 
     """
 
-    __props__ = ('ndim', 'ignore_border', 'mode')
+    __props__ = ('ignore_border', 'mode', 'ndim')
 
     @staticmethod
     def out_shape(imgshape, ds, ignore_border=False, st=None, padding=None, ndim=None):
@@ -722,7 +722,7 @@ class Pool(OpenMPOp):
 
 
 class PoolGrad(OpenMPOp):
-    __props__ = ('ndim', 'ignore_border', 'mode')
+    __props__ = ('ignore_border', 'mode', 'ndim')
 
     @staticmethod
     def out_shape(imgshape, ds, ignore_border=False, st=None, padding=None, ndim=None):
@@ -1505,7 +1505,7 @@ class AveragePoolGrad(PoolGrad):
 
 
 class DownsampleFactorMaxGradGrad(OpenMPOp):
-    __props__ = ('ndim', 'ignore_border', 'mode')
+    __props__ = ('ignore_border', 'mode', 'ndim')
 
     def __init__(self, ignore_border, mode='max', ndim=None, openmp=None):
         self.ndim = ndim