Adapted the new DownsampleFactorMaxGradGrad gradient.

theano/tensor/signal/pool.py

@@ -1089,13 +1089,18 @@ class DownsampleFactorMaxGradGrad(OpenMPOp):
         return [in_shapes[1]]
 
     def grad(self, inp, grads):
-        x, maxout, ggx = inp
+        x, maxout, ggx, ws, stride, pad = inp
         gz, = grads
         return [theano.tensor.zeros_like(x),
                 theano.tensor.zeros_like(maxout),
-                MaxPoolGrad(
-                    self.ds, ignore_border=self.ignore_border,
-                    st=self.st, padding=self.padding)(x, maxout, gz)]
+                MaxPoolGrad(ignore_border=self.ignore_border)(x, maxout, gz,
+                                                              ws, stride, pad),
+                DisconnectedType()(),
+                DisconnectedType()(),
+                DisconnectedType()()]
+
+    def connection_pattern(self, node):
+        return [[1], [1], [1], [0], [0], [0]]
 
     def c_code(self, node, name, inp, out, sub):
         if self.mode != 'max':

theano/tensor/signal/tests/test_pool.py

@@ -644,16 +644,12 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
             paddingsize = paddingsizes[i]
 
             def mp(input1, input2):
-                pooled_out = Pool(
-                    maxpoolsize, ignore_border=True,
-                    st=stridesize,
-                    padding=paddingsize,
-                    )(input1)
-                out = DownsampleFactorMaxGradGrad(
-                    ds=maxpoolsize,
-                    ignore_border=True,
-                    st=stridesize,
-                    padding=paddingsize)(input1, pooled_out, input2)
+                op1 = Pool(ignore_border=True)
+                pooled_out = op1(input1, maxpoolsize, stride=stridesize,
+                                 pad=paddingsize)
+                op2 = DownsampleFactorMaxGradGrad(ignore_border=True)
+                out = op2(input1, pooled_out, input2, ws=maxpoolsize,
+                          stride=stridesize, pad=paddingsize)
                 return out
             utt.verify_grad(mp, [imval1, imval2], rng=rng)