Again skip tests when cudnn isn't there

theano/gpuarray/tests/test_dnn.py

@@ -584,6 +584,8 @@ class TestDnnInferShapes(utt.InferShapeTester):
     conv_modes = ['conv', 'cross']
 
     def setUp(self):
+        if not dnn.dnn_available(test_ctx_name):
+            raise SkipTest(dnn.dnn_available.msg)
         super(TestDnnInferShapes, self).setUp()
         self.mode = mode_with_gpu
 
@@ -1032,6 +1034,8 @@ def test_dnn_conv_grad():
 
 
 def get_conv3d_test_cases():
+    if not dnn.dnn_available(test_ctx_name):
+        raise SkipTest(dnn.dnn_available.msg)
     # Every element of test_shapes follows the format
     # [input_shape, filter_shape, subsample, dilation]
     test_shapes = [[(128, 3, 5, 5, 5), (64, 3, 1, 2, 4), (1, 1, 1), (1, 1, 1)],
@@ -1117,14 +1121,18 @@ def run_conv_small_batched_vs_multicall(inputs_shape, filters_shape, batch_sub):
 
 
 def test_batched_conv_small():
-    # OK
+    if not dnn.dnn_available(test_ctx_name):
+        raise SkipTest(dnn.dnn_available.msg)
+
     yield (run_conv_small_batched_vs_multicall, (65536, 2, 2, 2), (1, 2, 2, 2), 5)
     # Should fail with cuDNN < V6020, but there's currently a workaround in `dnn_fwd.c` for that case.
     yield (run_conv_small_batched_vs_multicall, (65537, 2, 2, 2), (1, 2, 2, 2), 5)
 
 
 def test_batched_conv3d_small():
-    # OK
+    if not dnn.dnn_available(test_ctx_name):
+        raise SkipTest(dnn.dnn_available.msg)
+
     yield (run_conv_small_batched_vs_multicall, (65536, 2, 2, 2, 2), (1, 2, 2, 2, 2), 5)
     # Should fail with cuDNN < V6020, but there's currently a workaround in `dnn_fwd.c` for that case.
     yield (run_conv_small_batched_vs_multicall, (65537, 2, 2, 2, 2), (1, 2, 2, 2, 2), 5)
@@ -1529,11 +1537,15 @@ def dnn_reduction_strides(shp, shuffle, slice):
 
 
 def test_dnn_reduction_strides():
+    if not dnn.dnn_available(test_ctx_name):
+        raise SkipTest(dnn.dnn_available.msg)
     yield dnn_reduction_strides, (2, 3, 2), (1, 0, 2), slice(None, None, None)
     yield dnn_reduction_strides, (2, 3, 2), (0, 1, 2), slice(None, None, -1)
 
 
 def test_dnn_reduction_error():
+    if not dnn.dnn_available(test_ctx_name):
+        raise SkipTest(dnn.dnn_available.msg)
     nLoops = 5
     vec = np.arange(0, 10, dtype=np.float32)
     slow_output = np.zeros((5, 10))
@@ -2708,6 +2720,8 @@ class TestDnnConv3DRuntimeAlgorithms(TestDnnConv2DRuntimeAlgorithms):
 def test_conv_guess_once_with_dtypes():
     # This test checks that runtime conv algorithm selection does not raise any exception
     # when consecutive functions with different dtypes and precisions are executed.
+    if not dnn.dnn_available(test_ctx_name):
+        raise SkipTest(dnn.dnn_available.msg)
     utt.seed_rng()
     inputs_shape = (2, 3, 5, 5)
     filters_shape = (2, 3, 40, 4)