Add kernel flip to follow the behavior of Conv3D/ConvGrad3D

theano/sandbox/cuda/opt.py

@@ -1275,6 +1275,8 @@ def local_conv3d_fft(node):
         # Shuffle filters from (oc, 0, 1, t, ic) to (oc, ic, 0, 1, t)
         f = node.inputs[1]
         f = gpu_from_host(f.dimshuffle(0, 4, 1, 2, 3))
+        # filter flip
+        f = f[:,:,::-1,::-1,::-1]
         rval = conv3d_fft(x, f, border_mode='valid', pad_last_dim=True)
         # Shuffle from (oc, c, 0, 1, t) to (oc, 0, 1, t, c)
         return [rval.dimshuffle(0, 2, 3, 4, 1) + node.inputs[2]]
@@ -1301,6 +1303,8 @@ def local_convgrad3d_fft(node):
         # Shuffle dCdH from (b, 0, 1, t, oc) to (oc, b, 0, 1, t)
         f = node.inputs[3]
         f = f.dimshuffle(4, 0, 1, 2, 3)
+        # filter flip
+        f = f[:,:,::-1,::-1,::-1]
         rval = conv3d_fft(x, f, border_mode='valid', pad_last_dim=True)
         # Shuffle from (ic, oc, 0, 1, t) to (oc, 0, 1, t, ic)
         return [rval.dimshuffle(1, 2, 3, 4, 0)]
@@ -1329,7 +1333,7 @@ def local_convtransp3d_fft(node):
         f = f.dimshuffle(0, 4, 1, 2, 3)
         rval = conv3d_fft(f, x, border_mode='full', pad_last_dim=True)
         # Shuffle from (ic, b, 0, 1, t) to (b, 0, 1, t, ic)
-        return [rval.dimshuffle(0, 2, 3, 4, 1)]
+        return [rval.dimshuffle(0, 2, 3, 4, 1) + node.inputs[1]]
 
 gpu_optimizer.register("convtransp3d_fft", local_convtransp3d_fft)