Remove the bad compat for pickled DownsampleFactorMaxGrad and use a better one.

theano/tensor/signal/pool.py

@@ -808,8 +808,6 @@ class MaxPoolGrad(PoolGrad):
     def c_code_cache_version(self):
         return (0, 7)
 
-DownsampleFactorMaxGrad = MaxPoolGrad
-
 
 class AveragePoolGrad(PoolGrad):
 
@@ -817,7 +815,7 @@ class AveragePoolGrad(PoolGrad):
         assert mode in ['sum', 'average_inc_pad', 'average_exc_pad']
         PoolGrad.__init__(self, ds, ignore_border, st, padding, mode)
 
-    def make_node(self, x, gz):
+    def make_node(self, x, gz, dummy=None):
         # make_node should only be called by the grad function of
         # Pool, so these asserts should not fail.
         assert isinstance(x, Variable) and x.ndim == 4
@@ -892,6 +890,18 @@ class AveragePoolGrad(PoolGrad):
                      st=self.st, padding=self.padding, mode=self.mode)(ggx)]
 
 
+# This is for compatibility with pickled things.  It should go away at
+# some point.
+class DownsampleFactorMaxGrad(object):
+    def __new__(self, ds, ignore_border, st=None, padding=(0, 0), mode='max'):
+        if mode == 'max':
+            return MaxPoolGrad(ds=ds, ignore_border=ignore_border, st=st,
+                               padding=padding, mode='max')
+        else:
+            return AveragePoolGrad(ds=ds, ignore_border=ignore_border, st=st,
+                                   padding=padding, mode=mode)
+
+
 class DownsampleFactorMaxGradGrad(Op):
     __props__ = ('ds', 'ignore_border', 'st', 'padding', 'mode')
 
@@ -1060,19 +1070,3 @@ class DownsampleFactorMaxGradGrad(Op):
 
     def c_code_cache_version(self):
         return (0, 1)
-
-
-@register_canonicalize('fast_compile')
-@gof.local_optimizer([MaxPoolGrad])
-def local_average_pool_grad(node):
-    # To assure backward compatibility with
-    # DownsampleFactorMaxGrad
-    if (not isinstance(node.op, MaxPoolGrad) or node.op.mode not in
-            ['sum', 'average_exc_pad', 'average_inc_pad']):
-        return False
-    return [AveragePoolGrad(ds=node.op.ds,
-                            ignore_border=node.op.ignore_border,
-                            st=node.op.st,
-                            padding=node.op.padding,
-                            mode=node.op.mode)(node.inputs[0],
-                                               node.inputs[2])]

theano/tensor/signal/tests/test_pool.py

@@ -802,20 +802,17 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                   padding=(0, 0))(image)],
             [image_val], Pool)
 
-    def test_opt_max_to_average(self):
+    def test_DownsampleFactorMaxGrad(self):
         im = theano.tensor.tensor4()
         maxout = theano.tensor.tensor4()
         grad = theano.tensor.tensor4()
 
-        compilation_mode = theano.compile.get_default_mode().including(
-            'local_average_pool_grad')
-
         for mode in ['max', 'sum', 'average_inc_pad', 'average_exc_pad']:
             f = theano.function([im, maxout, grad],
                                 DownsampleFactorMaxGrad(ds=(3, 3),
                                                         ignore_border=False,
                                                         mode=mode)(im, maxout, grad),
-                                mode=compilation_mode)
+                                on_unused_input='ignore')
 
             if mode == 'max':
                 assert any(isinstance(n.op, MaxPoolGrad)