added mode='average_inc_pad' for test_pooling_opt test

theano/sandbox/cuda/tests/test_dnn.py

@@ -450,24 +450,30 @@ def test_pooling_opt():
     if not cuda.dnn.dnn_available():
         raise SkipTest(cuda.dnn.dnn_available.msg)
 
-    x = T.ftensor4()
+    x = T.matrix()
 
     f = theano.function(
         [x],
-        max_pool_2d(x, ds=(2, 2), ignore_border=True),
+        max_pool_2d(x, ds=(2, 2), mode='average_inc_pad',
+                ignore_border=True),
         mode=mode_with_gpu)
 
     assert any([isinstance(n.op, cuda.dnn.GpuDnnPool)
                 for n in f.maker.fgraph.toposort()])
 
+    f(numpy.zeros((10, 10), dtype='float32'))
+
     f = theano.function(
         [x],
-        T.grad(max_pool_2d(x, ds=(2, 2), ignore_border=True).sum(), x),
+        T.grad(max_pool_2d(x, ds=(2, 2), mode='average_inc_pad',
+                ignore_border=True).sum(), x),
         mode=mode_with_gpu.including("cudnn"))
 
     assert any([isinstance(n.op, cuda.dnn.GpuDnnPoolGrad)
                 for n in f.maker.fgraph.toposort()])
 
+    f(numpy.zeros((10, 10), dtype='float32'))
+
 
 class test_DnnSoftMax(test_nnet.test_SoftMax):
     gpu_op = dnn.GpuDnnSoftmax

theano/sandbox/gpuarray/tests/test_dnn.py

@@ -278,24 +278,29 @@ def test_pooling_opt():
     if not dnn.dnn_available():
         raise SkipTest(dnn.dnn_available.msg)
 
-    x = T.ftensor4()
+    x = T.matrix()
 
     f = theano.function(
         [x],
-        max_pool_2d(x, ds=(2, 2), ignore_border=True),
+        max_pool_2d(x, ds=(2, 2), mode='average_inc_pad',
+                ignore_border=True),
         mode=mode_with_gpu)
 
     assert any([isinstance(n.op, dnn.GpuDnnPool)
                 for n in f.maker.fgraph.toposort()])
 
+    f(numpy.zeros((10, 10), dtype='float32'))
+
     f = theano.function(
         [x],
-        T.grad(max_pool_2d(x, ds=(2, 2), ignore_border=True).sum(), x),
+        T.grad(max_pool_2d(x, ds=(2, 2), mode='average_inc_pad',
+                ignore_border=True).sum(), x),
         mode=mode_with_gpu.including("cudnn"))
 
     assert any([isinstance(n.op, dnn.GpuDnnPoolGrad)
                 for n in f.maker.fgraph.toposort()])
 
+    f(numpy.zeros((10, 10), dtype='float32'))
 
 def test_dnn_tag():
     """