Merge pull request #4696 from Sentient07/dilation_cleanup

theano/gpuarray/tests/test_abstractconv.py

@@ -21,12 +21,13 @@ class TestDnnConv2d(test_abstract_conv.BaseTestConv2d):
         # provide_shape is not used by the cuDNN impementation
         self.provide_shape = [False]
 
-    def tcase(self, i, f, s, b, flip, provide_shape):
+    def tcase(self, i, f, s, b, flip, provide_shape, fd=(1, 1)):
         if not dnn_available(test_ctx_name):
             raise SkipTest(dnn_available.msg)
         mode = mode_with_gpu
-
-        o = self.get_output_shape(i, f, s, b)
+        if fd != (1, 1):
+            raise SkipTest("Doesn't have CUDNN implementation")
+        o = self.get_output_shape(i, f, s, b, fd)
         self.run_fwd(inputs_shape=i, filters_shape=f, subsample=s,
                      verify_grad=True, mode=mode,
                      provide_shape=provide_shape, border_mode=b,

theano/tensor/nnet/abstract_conv.py

@@ -33,7 +33,7 @@ _logger = logging.getLogger("theano.tensor.nnet.abstract_conv")
 
 def get_conv_output_shape(image_shape, kernel_shape,
                           border_mode, subsample,
-                          filter_dilation=(1, 1)):
+                          filter_dilation=None):
     """
     This function compute the output shape of convolution operation.
 
@@ -66,6 +66,8 @@ def get_conv_output_shape(image_shape, kernel_shape,
     """
     bsize, imshp = image_shape[0], image_shape[2:]
     nkern, kshp = kernel_shape[0], kernel_shape[2:]
+    if filter_dilation is None:
+        filter_dilation = numpy.ones(len(subsample), dtype='int')
     if isinstance(border_mode, tuple):
         out_shp = tuple(get_conv_shape_1axis(
             imshp[i], kshp[i], border_mode[i],