Fixed 3d convolution params

theano/gpuarray/dnn.py

@@ -857,7 +857,6 @@ class GpuDnnConvGradI(DnnBase):
         if algo is None:
             algo = config.dnn.conv.algo_bwd_data
         self.algo = algo
-
         assert cudnn.cudnnConvolutionBwdDataAlgo_t.has_alias(self.algo) or self.algo in SUPPORTED_DNN_CONV_ALGO_RUNTIME
 
         self.conv_algo = cudnn.cudnnConvolutionBwdDataAlgo_t.CUDNN_CONVOLUTION_BWD_DATA_ALGO_0
@@ -1205,13 +1204,13 @@ def dnn_gradweight(img, topgrad, kerns_shp, border_mode='valid',
 
 def dnn_gradweight3d(img, topgrad, kerns_shp, border_mode='valid',
                      subsample=(1, 1, 1), dilation=(1, 1, 1), conv_mode='conv',
-                     precision=None, num_groups=1):
+                     precision=None, algo=None, num_groups=1):
     """
     3d version of dnn_gradweight
     """
     return dnn_gradweight(img, topgrad, kerns_shp, border_mode,
                           subsample, dilation, conv_mode, precision,
-                          num_groups)
+                          algo, num_groups)
 
 
 def dnn_gradinput(kerns, topgrad, img_shp, border_mode='valid',
@@ -1237,12 +1236,12 @@ def dnn_gradinput(kerns, topgrad, img_shp, border_mode='valid',
 
 def dnn_gradinput3d(kerns, topgrad, img_shp, border_mode='valid',
                     subsample=(1, 1, 1), dilation=(1, 1, 1), conv_mode='conv',
-                    precision=None, num_groups=1):
+                    precision=None, algo=None, num_groups=1):
     """
     3d version of `dnn_gradinput`.
     """
     return dnn_gradinput(kerns, topgrad, img_shp, border_mode, subsample,
-                         dilation, conv_mode, precision,
+                         dilation, conv_mode, precision, algo,
                          num_groups)