Fix test_dnn.py and make it pass.

theano/sandbox/gpuarray/tests/test_dnn.py

@@ -15,12 +15,12 @@ from theano.tensor.signal.downsample import MaxPoolGrad, AveragePoolGrad
 from .. import dnn
 from ..basic_ops import GpuAllocEmpty
 
-from .config import mode_with_gpu, mode_without_gpu
+from .config import mode_with_gpu, mode_without_gpu, test_ctx_name
 from . import test_nnet
 
 
 def test_dnn_conv_desc_merge():
-    if not dnn.dnn_available():
+    if not dnn.dnn_available(test_ctx_name):
         raise SkipTest(dnn.dnn_available.msg)
     kern_shp = T.as_tensor_variable(
         numpy.asarray([3, 1, 2, 2]).astype('int64'))
@@ -41,7 +41,7 @@ def test_dnn_conv_desc_merge():
 
 def test_dnn_conv_merge():
     # This test that we merge correctly multiple dnn_conv.
-    if not dnn.dnn_available():
+    if not dnn.dnn_available(test_ctx_name):
         raise SkipTest(dnn.dnn_available.msg)
     img_shp = [2, 5, 6, 8]
     kern_shp = [3, 5, 5, 6]
@@ -80,7 +80,7 @@ def test_dnn_conv_inplace():
     GpuAllocEmpty get merged together.
 
     """
-    if not dnn.dnn_available():
+    if not dnn.dnn_available(test_ctx_name):
         raise SkipTest(dnn.dnn_available.msg)
     img_shp = [2, 5, 6, 8]
     kern_shp = [3, 5, 5, 6]
@@ -105,7 +105,7 @@ def test_dnn_conv_inplace():
     assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
 
     # Test grad w op
-    out = GpuAllocEmpty(kern.dtype)(*kern.shape)
+    out = GpuAllocEmpty(kern.dtype, test_ctx_name)(*kern.shape)
     o1 = dnn.GpuDnnConvGradW()(img, kern, out, desc1)
     o2 = dnn.GpuDnnConvGradW()(img, kern, out, desc2)
     f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
@@ -116,7 +116,7 @@ def test_dnn_conv_inplace():
     assert len([n for n in topo if isinstance(n.op, GpuAllocEmpty)]) == 2
 
     # Test grad i op
-    out = GpuAllocEmpty(img.dtype)(*img.shape)
+    out = GpuAllocEmpty(img.dtype, test_ctx_name)(*img.shape)
     o1 = dnn.GpuDnnConvGradI()(img, kern, out, desc1)
     o2 = dnn.GpuDnnConvGradI()(img, kern, out, desc2)
     f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
@@ -163,7 +163,7 @@ def pool_2d_i2n(input, ds=(2, 2), strides=None,
 
 
 def test_pooling():
-    if not dnn.dnn_available():
+    if not dnn.dnn_available(test_ctx_name):
         raise SkipTest(dnn.dnn_available.msg)
 
     x = T.ftensor4()
@@ -269,7 +269,7 @@ def test_pooling():
 
 
 def test_pooling_opt():
-    if not dnn.dnn_available():
+    if not dnn.dnn_available(test_ctx_name):
         raise SkipTest(dnn.dnn_available.msg)
 
     x = T.fmatrix()
@@ -318,7 +318,7 @@ def test_dnn_tag():
             max_pool_2d(x, ds=(2, 2), ignore_border=True),
             mode=mode_with_gpu.including("cudnn"))
     except (AssertionError, RuntimeError):
-        assert not dnn.dnn_available()
+        assert not dnn.dnn_available(test_ctx_name)
         raised = True
     finally:
         theano.config.on_opt_error = old
@@ -327,7 +327,7 @@ def test_dnn_tag():
         logging.getLogger('theano').addHandler(theano.logging_default_handler)
 
     if not raised:
-        assert dnn.dnn_available()
+        assert dnn.dnn_available(test_ctx_name)
         assert any([isinstance(n.op, dnn.GpuDnnPool)
                     for n in f.maker.fgraph.toposort()])
 
@@ -338,7 +338,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
         self.mode = mode_with_gpu
 
     def test_softmax(self):
-        if not dnn.dnn_available():
+        if not dnn.dnn_available(test_ctx_name):
             raise SkipTest(dnn.dnn_available.msg)
         t = T.ftensor4('t')
         rand_tensor = numpy.asarray(
@@ -368,7 +368,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
         )
 
     def test_conv(self):
-        if not dnn.dnn_available():
+        if not dnn.dnn_available(test_ctx_name):
             raise SkipTest(dnn.dnn_available.msg)
         img = T.ftensor4('img')
         kerns = T.ftensor4('kerns')
@@ -406,7 +406,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
             )
 
     def test_conv_gradw(self):
-        if not dnn.dnn_available():
+        if not dnn.dnn_available(test_ctx_name):
             raise SkipTest(dnn.dnn_available.msg)
         img = T.ftensor4('img')
         kerns = T.ftensor4('kerns')
@@ -455,7 +455,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
             )
 
     def test_conv_gradi(self):
-        if not dnn.dnn_available():
+        if not dnn.dnn_available(test_ctx_name):
             raise SkipTest(dnn.dnn_available.msg)
         img = T.ftensor4('img')
         kerns = T.ftensor4('kerns')
@@ -499,7 +499,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
             )
 
     def test_pool(self):
-        if not dnn.dnn_available():
+        if not dnn.dnn_available(test_ctx_name):
             raise SkipTest(dnn.dnn_available.msg)
         img = T.ftensor4('img')
         img_val = numpy.asarray(
@@ -524,7 +524,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
             )
 
     def test_pool_grad(self):
-        if not dnn.dnn_available():
+        if not dnn.dnn_available(test_ctx_name):
             raise SkipTest(dnn.dnn_available.msg)
         img = T.ftensor4('img')
         img_grad = T.ftensor4('img_grad')
@@ -568,7 +568,7 @@ class TestDnnInferShapes(utt.InferShapeTester):
 
 # this has been a problem in the past
 def test_dnn_conv_border_mode():
-    if not dnn.dnn_available():
+    if not dnn.dnn_available(test_ctx_name):
         raise SkipTest(dnn.dnn_available.msg)
     img = T.ftensor4()
     kern = T.ftensor4()
@@ -580,7 +580,7 @@ def test_dnn_conv_border_mode():
 
 
 def test_dnn_conv_alpha_output_merge():
-    if not dnn.dnn_available():
+    if not dnn.dnn_available(test_ctx_name):
         raise SkipTest(dnn.dnn_available.msg)
     img = T.ftensor4()
     kern = T.ftensor4()
@@ -678,7 +678,7 @@ def test_dnn_conv_grad():
 
 
 def test_version():
-    if not dnn.dnn_available():
+    if not dnn.dnn_available(test_ctx_name):
         raise SkipTest(dnn.dnn_available.msg)
     assert isinstance(dnn.version(), int)