change get_conv_grad* functions to support num_groups

theano/tensor/nnet/abstract_conv.py

@@ -138,7 +138,8 @@ def get_conv_shape_1axis(image_shape, kernel_shape, border_mode,
 
 def get_conv_gradweights_shape(image_shape, top_shape,
                                border_mode, subsample,
-                               filter_dilation=None):
+                               filter_dilation=None,
+                               num_groups=1):
     """
     This function tries to compute the kernel shape of convolution gradWeights.
 
@@ -166,6 +167,8 @@ def get_conv_gradweights_shape(image_shape, top_shape,
     filter_dilation: tuple of int (symbolic or numeric). Its two or three
         elements correspond respectively to the dilation on height and
         width axis.
+    num_groups: An int which specifies the number of separate groups to
+        be divided into.
 
     Returns
     -------
@@ -180,6 +183,8 @@ def get_conv_gradweights_shape(image_shape, top_shape,
 
     if filter_dilation is None:
         filter_dilation = np.ones(len(subsample), dtype='int')
+    if num_groups > 1 and len(subsample) == 2:
+        nchan = nchan // num_groups
 
     if isinstance(border_mode, tuple):
         out_shp = tuple(get_conv_gradweights_shape_1axis(
@@ -244,7 +249,8 @@ def get_conv_gradweights_shape_1axis(image_shape, top_shape, border_mode,
 
 def get_conv_gradinputs_shape(kernel_shape, top_shape,
                               border_mode, subsample,
-                              filter_dilation=None):
+                              filter_dilation=None,
+                              num_groups=1):
     """
     This function tries to compute the image shape of convolution gradInputs.
 
@@ -272,6 +278,8 @@ def get_conv_gradinputs_shape(kernel_shape, top_shape,
     filter_dilation: tuple of int (symbolic or numeric). Its two or three
         elements correspond respectively to the dilation on height and
         width axis.
+    num_groups: An int which specifies the number of separate groups to
+        be divided into.
 
     Returns
     -------
@@ -285,6 +293,8 @@ def get_conv_gradinputs_shape(kernel_shape, top_shape,
 
     if filter_dilation is None:
         filter_dilation = np.ones(len(subsample), dtype='int')
+    if num_groups > 1 and len(subsample) == 2:
+        nkern = nkern * num_groups
 
     if isinstance(border_mode, tuple):
         out_shp = tuple(get_conv_gradinputs_shape_1axis(