Do not check for non-finite values when we have -inf

theano/sandbox/cuda/tests/test_dnn.py

@@ -412,6 +412,13 @@ def test_pooling3d():
     if not cuda.dnn.dnn_available() or cuda.dnn.version() < (3000, 3000):
         raise SkipTest(cuda.dnn.dnn_available.msg)
 
+    # For max pooling pool3d2d explicitly pads the input with
+    # -inf. Because of this, the compilation mode for the function
+    # that uses pool3d2d should not check for infinite values or
+    # it will falsely believe there is a error in the graph.
+    mode_without_gpu2 = mode_without_gpu.including()
+    mode_without_gpu2.check_isfinite = False
+
     # 'average_exc_pad' is disabled for versions < 4004
     if cuda.dnn.version() < (4004, 4004):
         modes = ('max', 'average_inc_pad')
@@ -447,13 +454,6 @@ def test_pooling3d():
                                 strides=(stride, stride, stride),
                                 pad=pad, pool_func=func)
 
-                # For max pooling pool3d2d explicitly pads the input with
-                # -inf. Because of this, the compilation mode for the function
-                # that uses pool3d2d should not check for infinite values or
-                # it will falsely believe there is a error in the graph.
-                mode_without_gpu2 = mode_without_gpu.including()
-                mode_without_gpu2.check_isfinite = False
-
                 f1 = theano.function([x], out1, mode=mode_with_gpu)
                 assert any([isinstance(node.op, cuda.dnn.GpuDnnPool)
                             for node in f1.maker.fgraph.apply_nodes])
@@ -512,7 +512,7 @@ def test_pooling3d():
                            strides=(stride, stride, stride),
                            pad=pad, pool_func=func)
             fc = theano.function([x], theano.grad(out.sum(), x),
-                                 mode=mode_without_gpu)
+                                 mode=mode_without_gpu2)
             c_out = fc(data)
             utt.assert_allclose(c_out, g_out)