Implement GpuImages2Neibs mode ignore_border.

theano/sandbox/cuda/neighbours.py

@@ -13,8 +13,9 @@ if cuda_available:
 
 class GpuImages2Neibs(Images2Neibs, GpuOp):
     def __init__(self, mode='valid'):
-        if mode not in ['valid', 'wrap_centered']:
-            raise NotImplementedError("Only the mode valid and wrap_centered"
+        if mode not in ['valid', 'ignore_borders', 'wrap_centered']:
+            raise NotImplementedError("Only the mode valid, ignore_borders"
+                                      " and wrap_centered"
                                       " have been implemented for the op"
                                       " GpuImages2Neibs")
         self.mode = mode
@@ -277,6 +278,11 @@ class GpuImages2Neibs(Images2Neibs, GpuOp):
                 grid_c = 1+(((CudaNdarray_HOST_DIMS(%(ten4)s))[2]-c)/step_x);
                 //number of patch in width
                 grid_d = 1+(((CudaNdarray_HOST_DIMS(%(ten4)s))[3]-d)/step_y);
+            }else if ( "%(mode)s" == "ignore_borders") {
+                //number of patch in height
+                grid_c = 1+(((CudaNdarray_HOST_DIMS(%(ten4)s))[2]-c)/step_x);
+                //number of patch in width
+                grid_d = 1+(((CudaNdarray_HOST_DIMS(%(ten4)s))[3]-d)/step_y);
             }else{
                 PyErr_Format(PyExc_TypeError,
                              "Images2Neibs: unknow mode '%(mode)s'");
@@ -403,7 +409,8 @@ def gpu_images2neibs(ten4, neib_shape, neib_step=None, mode='valid'):
 def use_gpu_images2neibs(node):
     if (type(node.op) is Images2Neibs and
         node.inputs[0].dtype == 'float32' and
-        node.op.mode in ['valid', 'wrap_centered']):
+        node.op.mode in ['valid', 'ignore_borders',
+                         'wrap_centered']):
         return [host_from_gpu(gpu_images2neibs(gpu_from_host(node.inputs[0]),
                                                node.inputs[1], node.inputs[2],
                                                mode=node.op.mode))]

theano/sandbox/test_neighbours.py

@@ -41,9 +41,8 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
                     g = function([],
                                  neibs2images(neibs, neib_shape, images.shape),
                                  mode=self.mode)
-                    if border in ['valid']:
-                        assert any([isinstance(node.op, self.op)
-                                    for node in f.maker.fgraph.toposort()])
+                    assert any([isinstance(node.op, self.op)
+                                for node in f.maker.fgraph.toposort()])
 
                     #print g()
                     assert numpy.allclose(images.get_value(borrow=True), g())
@@ -59,6 +58,8 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
             for border in ['valid', 'ignore_borders']:
                 f = function([], images2neibs(images, neib_shape, mode=border),
                              mode=self.mode)
+                assert any([isinstance(node.op, self.op)
+                            for node in f.maker.fgraph.toposort()])
 
                 #print images.get_value(borrow=True)
                 neibs = f()
@@ -107,9 +108,8 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
                              mode=self.mode)
 
                 neibs = f()
-                if border in ['valid']:
-                    assert self.op in [type(node.op)
-                                       for node in f.maker.fgraph.toposort()]
+                assert self.op in [type(node.op)
+                                   for node in f.maker.fgraph.toposort()]
 
                 assert numpy.allclose(neibs,
               [[  0,   1,   2,   5,   6,   7,  10,  11,  12],
@@ -162,6 +162,8 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
                              images2neibs(images, neib_shape,
                                           mode='ignore_borders'),
                              mode=self.mode)
+                assert self.op in [type(node.op)
+                                   for node in f.maker.fgraph.toposort()]
                 f()
 
     def test_neibs_wrap_centered_step_manual(self):