add support for non default filter dilation in gradinputs alternative

theano/gpuarray/dnn.py

@@ -1014,7 +1014,7 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
         conv = GpuDnnConvGradW()(img, kerns, out, desc)
         return as_gpuarray_variable(conv.dimshuffle(1, 0, 2, 3), ctx_name)
 
-    elif (border_mode == 'full' and subsample == (1, 1) and dilation == (1, 1) and
+    elif (border_mode == 'full' and subsample == (1, 1) and
           direction_hint != 'forward!' and num_groups == 1):
         # Special case: We can be faster by using GpuDnnConvGradI to compute
         # the full convolution as the backward pass of a valid convolution.
@@ -1024,11 +1024,11 @@ def dnn_conv(img, kerns, border_mode='valid', subsample=(1, 1), dilation=(1, 1),
         conv_mode = 'cross' if conv_mode == 'conv' else 'conv'
         out_shp = (shape_i(img, 0, fgraph),
                    shape_i(kerns, 1, fgraph),
-                   shape_i(img, 2, fgraph) + shape_i(kerns, 2, fgraph) - 1,
-                   shape_i(img, 3, fgraph) + shape_i(kerns, 3, fgraph) - 1)
+                   shape_i(img, 2, fgraph) + (shape_i(kerns, 2, fgraph) - 1) * dilation[0],
+                   shape_i(img, 3, fgraph) + (shape_i(kerns, 3, fgraph) - 1) * dilation[1])
         out_shp = assert_conv_shape(out_shp)
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
-        desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1), dilation=(1, 1),
+        desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1), dilation=dilation,
                               conv_mode=conv_mode, precision=precision)(kerns.shape)
         return GpuDnnConvGradI()(kerns, img, out, desc)
 
@@ -1133,7 +1133,7 @@ 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 dilation == (1, 1, 1) and
+    elif (border_mode == 'full' and subsample == (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.
@@ -1143,12 +1143,12 @@ def dnn_conv3d(img, kerns, border_mode='valid', subsample=(1, 1, 1), dilation=(1
         conv_mode = 'cross' if conv_mode == 'conv' else 'conv'
         out_shp = (shape_i(img, 0, fgraph),
                    shape_i(kerns, 1, fgraph),
-                   shape_i(img, 2, fgraph) + shape_i(kerns, 2, fgraph) - 1,
-                   shape_i(img, 3, fgraph) + shape_i(kerns, 3, fgraph) - 1,
-                   shape_i(img, 4, fgraph) + shape_i(kerns, 4, fgraph) - 1)
+                   shape_i(img, 2, fgraph) + (shape_i(kerns, 2, fgraph) - 1) * dilation[0],
+                   shape_i(img, 3, fgraph) + (shape_i(kerns, 3, fgraph) - 1) * dilation[1],
+                   shape_i(img, 4, fgraph) + (shape_i(kerns, 4, fgraph) - 1) * dilation[2])
         out_shp = assert_conv_shape(out_shp)
         out = GpuAllocEmpty(dtype=img.dtype, context_name=ctx_name)(*out_shp)
-        desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1, 1), dilation=(1, 1, 1),
+        desc = GpuDnnConvDesc(border_mode='valid', subsample=(1, 1, 1), dilation=dilation,
                               conv_mode=conv_mode, precision=precision)(kerns.shape)
         return GpuDnnConvGradI()(kerns, img, out, desc)