solve conflicts

theano/sandbox/gpuarray/dnn.py

@@ -40,9 +40,9 @@ def raise_no_cudnn(msg="CuDNN is required for convolution and pooling"):
 
 def _dnn_check_compile():
     preambule = """
-#include <stdio.h>
-#include <cudnn.h>
-#include <cudnn_helper.h>
+# include <stdio.h>
+# include <cudnn.h>
+# include <cudnn_helper.h>
 """
 
     # No need for the context in here since we won't execute that code
@@ -201,9 +201,9 @@ class DnnVersion(Op):
 
     def c_support_code(self):
         return """
-#if PY_MAJOR_VERSION >= 3
-#define PyInt_FromLong PyLong_FromLong
-#endif
+# if PY_MAJOR_VERSION >= 3
+# define PyInt_FromLong PyLong_FromLong
+# endif
 """
 
     def make_node(self):
@@ -1077,6 +1077,11 @@ class GpuDnnPool(DnnBase):
         if mode == 'average':
             mode = 'average_inc_pad'
         assert mode in ('max', 'average_inc_pad', 'average_exc_pad')
+<< << << < HEAD
+== == == =
+        if version() == -1:
+            raise Exception("cudnn v1 do not support average_exc_pad")
+>>>>>> > cb3fbfe94620d4938c10ac2e20d43f8a286e3856
         self.mode = mode
 
     def get_op_params(self):

theano/sandbox/gpuarray/tests/test_dnn.py

@@ -284,11 +284,11 @@ def test_pooling_with_tensor_vars():
     mode = 'max'
 
     def fn(x):
-        dnn_op = dnn.dnn_pool(
-            x, ws=ws,
-            stride=st,
-            pad=pad,
-            mode=mode)
+        dnn_op = dnn.dnn_pool(x,
+                              ws=ws,
+                              stride=st,
+                              pad=pad,
+                              mode=mode)
         return dnn_op
 
     for shp in [(1, 1, 2, 2),
@@ -299,32 +299,28 @@ def test_pooling_with_tensor_vars():
             cast_to_output_type=False,
             mode=mode_with_gpu)
 
+    out2 = pool_2d_i2n(x, ds=(2, 2), strides=(1, 1),
+                       pad=(0, 0),
+                       pool_function=T.max)
+
     mode_without_gpu2 = mode_without_gpu.including()
     mode_without_gpu2.check_isfinite = False
 
-    f_gpu = theano.function([x], fn(x), mode=mode_with_gpu)
+    f1 = theano.function([x], fn(x), mode=mode_with_gpu)
     assert any([isinstance(node.op, dnn.GpuDnnPool)
-                for node in f_gpu.maker.fgraph.apply_nodes])
-
-    i = 1
+                for node in f1.maker.fgraph.apply_nodes])
+    f2 = theano.function([x], out2, mode=mode_without_gpu2)
+    assert not any([isinstance(node.op, dnn.GpuDnnPool)
+                    for node in f2.maker.fgraph.apply_nodes])
     for shp in [(1, 10, 100, 100),
                 (1, 3, 99, 99),
-                (32, 1, 147, 197)]:
+                (32, 1, 147, 197),
+                ]:
         data = numpy.random.normal(0, 1, shp).astype("float32")
-        out = pool_2d_i2n(x, ds=(i, i), strides=(1, 1),
-                          pad=(0, 0),
-                          pool_function=T.max)
-
-        f_cpu = theano.function([x], out, mode=mode_without_gpu2)
-        assert not any([isinstance(node.op, dnn.GpuDnnPool)
-                        for node in f_cpu.maker.fgraph.apply_nodes])
-
-        # Change the window size dynamically for gpu op
-        ws.set_value(numpy.array([i, i]).astype('int32'))
-        a = f_gpu(data).__array__()
-        b = f_cpu(data).__array__()
+        a = f1(data).__array__()
+
+        b = f2(data).__array__()
         utt.assert_allclose(a, b)
-        i += 1
 
 
 def test_pooling_opt():