Return max pool grad grad for now instead of rop

theano/gpuarray/pool.py

@@ -124,9 +124,13 @@ class GpuPool(CGpuKernelBase):
         # return None for those.
         if eval_points[0] is None:
             return [None]
+        z = self(*inputs)
         x, ws, stride, pad = inputs
-        rop = GpuMaxPoolRop(ignore_border=self.ignore_border)
-        return [rop(x, eval_points[0], ws, stride=stride, pad=pad)]
+        return [
+            GpuDownsampleFactorMaxGradGrad(self.ignore_border, self.mode,
+                                           self.ndim)(x, z, eval_points[0], ws,
+                                                      stride, pad)
+        ]
 
 
 class GpuMaxPoolGrad(CGpuKernelBase):

theano/gpuarray/tests/test_pool.py

@@ -8,13 +8,13 @@ import numpy
 import theano
 from theano import gradient
 from theano import tensor
-from theano.tensor.signal.pool import (Pool, MaxPoolGrad, AveragePoolGrad, MaxPoolRop,
+from theano.tensor.signal.pool import (Pool, MaxPoolGrad, AveragePoolGrad,
                                        DownsampleFactorMaxGradGrad)
 from theano.tests import unittest_tools as utt
 
 from .config import mode_with_gpu, mode_without_gpu
 from .test_basic_ops import rand
-from ..pool import (GpuPool, GpuMaxPoolGrad, GpuAveragePoolGrad, GpuMaxPoolRop,
+from ..pool import (GpuPool, GpuMaxPoolGrad, GpuAveragePoolGrad,
                     GpuDownsampleFactorMaxGradGrad)
 
 
@@ -144,11 +144,11 @@ def test_pool2d():
                 gr2 = theano.function([], tensor.Rop(a_pooled, a, ea), mode=ref_mode)
 
                 assert any([
-                    isinstance(node.op, GpuMaxPoolRop)
+                    isinstance(node.op, GpuDownsampleFactorMaxGradGrad)
                     for node in gr.maker.fgraph.toposort()
                 ])
                 assert any([
-                    isinstance(node.op, MaxPoolRop)
+                    isinstance(node.op, DownsampleFactorMaxGradGrad)
                     for node in gr2.maker.fgraph.toposort()
                 ])
                 assert numpy.allclose(gr(), gr2()), (shp, ws, st, pad, mode, ignore_border)
@@ -254,11 +254,11 @@ def test_pool3d():
                 gr2 = theano.function([], tensor.Rop(a_pooled, a, ea), mode=ref_mode)
 
                 assert any([
-                    isinstance(node.op, GpuMaxPoolRop)
+                    isinstance(node.op, GpuDownsampleFactorMaxGradGrad)
                     for node in gr.maker.fgraph.toposort()
                 ])
                 assert any([
-                    isinstance(node.op, MaxPoolRop)
+                    isinstance(node.op, DownsampleFactorMaxGradGrad)
                     for node in gr2.maker.fgraph.toposort()
                 ])
                 assert numpy.allclose(gr(), gr2()), (shp, ws, st, pad, mode, ignore_border)

theano/tensor/signal/pool.py

@@ -592,9 +592,13 @@ class Pool(OpenMPOp):
         # return None for those.
         if eval_points[0] is None:
             return [None]
+        z = self(*inputs)
         x, ws, stride, pad = inputs
-        rop = MaxPoolRop(ignore_border=self.ignore_border, ndim=self.ndim)
-        return [rop(x, eval_points[0], ws, stride=stride, pad=pad)]
+        return [
+            DownsampleFactorMaxGradGrad(self.ignore_border, self.mode,
+                                        self.ndim)(x, z, eval_points[0], ws,
+                                                   stride, pad)
+        ]
 
     def c_headers(self):
         headers = ['<algorithm>']