Fix import and gpuarray/dnn

theano/sandbox/gpuarray/dnn.py

@@ -13,7 +13,7 @@ from theano.compile.ops import shape_i
 from theano.configparser import AddConfigVar, EnumStr, StrParam
 from theano.tensor.nnet import SoftmaxGrad
 from theano.tensor.signal.downsample import (
-    DownsampleFactorMax, DownsampleFactorMaxGrad)
+    DownsampleFactorMax, MaxPoolGrad, AveragePoolGrad)
 
 from . import pygpu, init_dev
 from .basic_ops import (as_gpuarray_variable,
@@ -1654,7 +1654,7 @@ def local_pool_dnn_alternative(node):
 
 
 @register_opt('cudnn')
-@op_lifter([DownsampleFactorMaxGrad])
+@op_lifter([MaxPoolGrad])
 def local_pool_dnn_grad_stride(node):
     if not dnn_available():
         return
@@ -1672,6 +1672,24 @@ def local_pool_dnn_grad_stride(node):
                             gpu_contiguous(inp_grad),
                             desc)
 
+@register_opt('cudnn')
+@op_lifter([AveragePoolGrad])
+def local_avg_pool_dnn_grad_stride(node):
+    if not dnn_available():
+        return
+    if not node.op.ignore_border:
+        return
+    inp, inp_grad = node.inputs
+    ds = node.op.ds
+    st = node.op.st
+    pad = node.op.padding
+    mode = node.op.mode
+
+    desc = GpuDnnPoolDesc(ws=ds, stride=st, mode=mode, pad=pad)()
+    return GpuDnnPoolGrad()(gpu_contiguous(inp),
+                            gpu_contiguous(numpy.empty((1,1,1,1), dtype=numpy.float32)),
+                            gpu_contiguous(inp_grad),
+                            desc)
 
 @register_opt('cudnn')
 @local_optimizer([GpuSoftmax])

theano/sandbox/gpuarray/tests/test_dnn.py

@@ -10,7 +10,7 @@ import theano.tensor as T
 import theano.tests.unittest_tools as utt
 from theano.sandbox.neighbours import images2neibs
 from theano.tensor.signal.downsample import max_pool_2d
-from theano.tensor.signal.downsample import DownsampleFactorMaxGrad
+from theano.tensor.signal.downsample import MaxPoolGrad, AveragePoolGrad
 
 from .. import dnn
 from ..basic_ops import GpuAllocEmpty
@@ -264,8 +264,12 @@ def test_pooling():
                               ignore_border=True, mode=mode)
             fc = theano.function([x], theano.grad(out.sum(), x),
                                  mode=mode_without_gpu)
-            assert any([isinstance(node.op, DownsampleFactorMaxGrad)
-                        for node in fc.maker.fgraph.toposort()])
+            if mode == 'max':
+                assert any([isinstance(node.op, MaxPoolGrad)
+                            for node in fc.maker.fgraph.toposort()])
+            else:
+                assert any([isinstance(node.op, AveragePoolGrad)
+                            for node in fc.maker.fgraph.toposort()])
             c_out = fc(data)
             assert numpy.allclose(c_out, g_out)