Proper input shapes for 3D dilated conv tests.

theano/gpuarray/dnn.py

@@ -1121,7 +1121,7 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1), dilation=(1
         conv = GpuDnnConvGradW()(img, kerns, out, desc)
         return as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3, 4), ctx_name)
 
-    elif (border_mode == 'full' and subsample == (1, 1, 1) and
+    elif (border_mode == 'full' and subsample == (1, 1, 1) and dilation == (1, 1, 1) and
           direction_hint != 'forward!'):
         # Special case: We can be faster by using GpuDnnConvGradI to compute
         # the full convolution as the backward pass of a valid convolution.

theano/gpuarray/tests/test_dnn.py

@@ -656,17 +656,15 @@ class TestDnnInferShapes(utt.InferShapeTester):
 
     @parameterized.expand(product(border_modes, conv_modes), utt.custom_name_func)
     def test_conv3d_none(self, border_mode, conv_mode):
-        # CUDNN docs don't say that 3D conv can't handle dilation, but it returns
-        # CUDNN_STATUS_NOT_SUPPORTED if you try it.
         self._test_conv(T.tensor5('img'),
                         T.tensor5('kerns'),
                         T.tensor5('out'),
-                        np.random.rand(10, 2, 6, 4, 11),
+                        np.random.rand(10, 2, 15, 16, 17),
                         np.random.rand(8, 2, 4, 3, 1),
                         border_mode,
                         conv_mode,
                         [(1, 1, 1), (2, 2, 2)],
-                        [(1, 1, 1)],
+                        [(1, 1, 1), (2, 2, 2)],
                         'none')
 
     def _test_conv_gradw(self, img, topgrad, kerns, img_shape, kerns_shape, border_mode, conv_mode, subsamples, dilations):
@@ -1025,6 +1023,8 @@ def get_conv3d_test_cases():
                    [(8, 4, 20, 12, 15), (5, 4, 6, 12, 4), (2, 2, 2), (1, 1, 1)],
                    [(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 3, 3), (1, 1, 1)],
                    [(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 2, 1), (1, 1, 1)],
+                   [(8, 1, 20, 12, 15), (5, 1, 6, 3, 4), (1, 1, 2), (3, 2, 1)],
+                   [(8, 1, 20, 12, 15), (5, 1, 6, 3, 4), (2, 2, 1), (1, 2, 3)],
                    # Test with 1x1x1 filters
                    [(8, 1, 10, 10, 10), (10, 1, 1, 1, 1), (1, 1, 1), (1, 1, 1)],
                    # Test with dimensions larger than 1024 (thread block dim)
@@ -1042,7 +1042,8 @@ def get_conv3d_test_cases():
     test_shapes_full = [[(6, 2, 2, 2, 2), (4, 2, 3, 1, 1), (1, 1, 1), (1, 1, 1)],
                         [(6, 2, 2, 2, 2), (4, 2, 1, 3, 1), (1, 1, 1), (1, 1, 1)],
                         [(6, 2, 2, 2, 2), (4, 2, 1, 1, 3), (1, 1, 1), (1, 1, 1)],
-                        [(6, 2, 2, 2, 2), (4, 2, 5, 5, 5), (1, 1, 1), (1, 1, 1)]]
+                        [(6, 2, 2, 2, 2), (4, 2, 5, 5, 5), (1, 1, 1), (1, 1, 1)],
+                        [(6, 2, 2, 2, 2), (4, 2, 5, 5, 5), (1, 1, 1), (3, 2, 1)]]
     border_modes = ['valid', 'full', 'half', (1, 2, 3), (3, 2, 1), 1, 2]
     conv_modes = ['conv', 'cross']