Refactor how the parameters are iterated over to reduce the total time of the…

theano/sandbox/cuda/tests/test_abstractconv.py

@@ -24,70 +24,59 @@ else:
 class TestDnnConv2d(test_abstract_conv.TestConv2d):
     def setUp(self):
         super(TestDnnConv2d, self).setUp()
+        # provide_shape is not used by the CuDNN impementation
+        self.provide_shape = [False]
         self.shared = gpu_shared
 
-    def test_dnn_conv(self):
+    def tcase(self, i, f, s, b, flip, provide_shape):
         if not dnn_available():
             raise SkipTest(cuda.dnn.dnn_available.msg)
         mode = mode_with_gpu
-        # provide_shape is not used by the CuDNN impementation
-        provide_shape = False
-        for (i, f), s, b, flip in itertools.product(
-                zip(self.inputs_shapes, self.filters_shapes),
-                self.subsamples,
-                self.border_modes,
-                self.filter_flip):
-            o = self.get_output_shape(i, f, s, b)
-            self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
-                         verify_grad=True, mode=mode,
-                         provide_shape=provide_shape, border_mode=b,
-                         filter_flip=flip, target_op=GpuDnnConv)
-            self.run_gradweight(inputs_shape=i, filters_shape=f,
-                                output_shape=o, subsample=s,
-                                verify_grad=True, mode=mode,
-                                provide_shape=provide_shape, border_mode=b,
-                                filter_flip=flip, target_op=GpuDnnConvGradW)
-            self.run_gradinput(inputs_shape=i, filters_shape=f,
-                               output_shape=o, subsample=s,
-                               verify_grad=True, mode=mode,
-                               provide_shape=provide_shape, border_mode=b,
-                               filter_flip=flip, target_op=GpuDnnConvGradI)
+        o = self.get_output_shape(i, f, s, b)
+        self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
+                     verify_grad=True, mode=mode,
+                     provide_shape=provide_shape, border_mode=b,
+                     filter_flip=flip, target_op=GpuDnnConv)
+        self.run_gradweight(inputs_shape=i, filters_shape=f,
+                            output_shape=o, subsample=s,
+                            verify_grad=True, mode=mode,
+                            provide_shape=provide_shape, border_mode=b,
+                            filter_flip=flip, target_op=GpuDnnConvGradW)
+        self.run_gradinput(inputs_shape=i, filters_shape=f,
+                           output_shape=o, subsample=s,
+                           verify_grad=True, mode=mode,
+                           provide_shape=provide_shape, border_mode=b,
+                           filter_flip=flip, target_op=GpuDnnConvGradI)
 
 
 class TestCorrMMConv2d(test_abstract_conv.TestConv2d):
     def setUp(self):
         super(TestCorrMMConv2d, self).setUp()
         self.shared = gpu_shared
+        self.mode = mode_with_gpu.excluding('cudnn')
 
-    def test_gpucorrmm_conv(self):
-        mode = mode_with_gpu.excluding('cudnn')
-        for (i, f), s, b, flip, provide_shape in itertools.product(
-                zip(self.inputs_shapes, self.filters_shapes),
-                self.subsamples,
-                self.border_modes,
-                self.filter_flip,
-                [False, True]):
-
-            o = self.get_output_shape(i, f, s, b)
-            self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
-                         verify_grad=True, mode=mode,
-                         provide_shape=provide_shape, border_mode=b,
-                         filter_flip=flip,
-                         target_op=(GpuCorrMM,
-                                    GpuCorrMM_gradWeights,
-                                    GpuCorrMM_gradInputs))
-            self.run_gradweight(inputs_shape=i, filters_shape=f,
-                                output_shape=o, subsample=s,
-                                verify_grad=True, mode=mode,
-                                provide_shape=provide_shape, border_mode=b,
-                                filter_flip=flip,
-                                target_op=GpuCorrMM_gradWeights)
-            self.run_gradinput(inputs_shape=i, filters_shape=f,
-                               output_shape=o, subsample=s,
-                               verify_grad=True, mode=mode,
-                               provide_shape=provide_shape, border_mode=b,
-                               filter_flip=flip,
-                               target_op=GpuCorrMM_gradInputs)
+    def test_gpucorrmm_conv(self, i, f, s, b, flip, provide_shape):
+        mode = self.mode
+        o = self.get_output_shape(i, f, s, b)
+        self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
+                     verify_grad=True, mode=mode,
+                     provide_shape=provide_shape, border_mode=b,
+                     filter_flip=flip,
+                     target_op=(GpuCorrMM,
+                                GpuCorrMM_gradWeights,
+                                GpuCorrMM_gradInputs))
+        self.run_gradweight(inputs_shape=i, filters_shape=f,
+                            output_shape=o, subsample=s,
+                            verify_grad=True, mode=mode,
+                            provide_shape=provide_shape, border_mode=b,
+                            filter_flip=flip,
+                            target_op=GpuCorrMM_gradWeights)
+        self.run_gradinput(inputs_shape=i, filters_shape=f,
+                           output_shape=o, subsample=s,
+                           verify_grad=True, mode=mode,
+                           provide_shape=provide_shape, border_mode=b,
+                           filter_flip=flip,
+                           target_op=GpuCorrMM_gradInputs)
 
 
 class TestDnnConvTypes(test_abstract_conv.TestConvTypes):

theano/sandbox/gpuarray/tests/test_abstractconv.py

@@ -15,34 +15,29 @@ class TestDnnConv2d(test_abstract_conv.TestConv2d):
     def setUp(self):
         super(TestDnnConv2d, self).setUp()
         self.shared = gpuarray_shared_constructor
+        # provide_shape is not used by the CuDNN impementation
+        self.provide_shape = [False]
 
-    def test_dnn_conv(self):
+    def tcase(self, i, f, s, b, flip, provide_shape):
         if not dnn_available(test_ctx_name):
             raise SkipTest(dnn_available.msg)
         mode = mode_with_gpu
-        # provide_shape is not used by the CuDNN impementation
-        provide_shape = False
-
-        for (i, f), s, b, flip in itertools.product(
-                zip(self.inputs_shapes, self.filters_shapes),
-                self.subsamples,
-                self.border_modes,
-                self.filter_flip):
-            o = self.get_output_shape(i, f, s, b)
-            self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
-                         verify_grad=True, mode=mode,
-                         provide_shape=provide_shape, border_mode=b,
-                         filter_flip=flip, target_op=GpuDnnConv)
-            self.run_gradweight(inputs_shape=i, filters_shape=f,
-                                output_shape=o, subsample=s,
-                                verify_grad=True, mode=mode,
-                                provide_shape=provide_shape, border_mode=b,
-                                filter_flip=flip, target_op=GpuDnnConvGradW)
-            self.run_gradinput(inputs_shape=i, filters_shape=f,
-                               output_shape=o, subsample=s,
-                               verify_grad=True, mode=mode,
-                               provide_shape=provide_shape, border_mode=b,
-                               filter_flip=flip, target_op=GpuDnnConvGradI)
+
+        o = self.get_output_shape(i, f, s, b)
+        self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
+                     verify_grad=True, mode=mode,
+                     provide_shape=provide_shape, border_mode=b,
+                     filter_flip=flip, target_op=GpuDnnConv)
+        self.run_gradweight(inputs_shape=i, filters_shape=f,
+                            output_shape=o, subsample=s,
+                            verify_grad=True, mode=mode,
+                            provide_shape=provide_shape, border_mode=b,
+                            filter_flip=flip, target_op=GpuDnnConvGradW)
+        self.run_gradinput(inputs_shape=i, filters_shape=f,
+                           output_shape=o, subsample=s,
+                           verify_grad=True, mode=mode,
+                           provide_shape=provide_shape, border_mode=b,
+                           filter_flip=flip, target_op=GpuDnnConvGradI)
 
 
 class TestDnnConvTypes(test_abstract_conv.TestConvTypes):