Disabled the grad on Images2Neib as it was doing the inverse and not the grad!

theano/sandbox/neighbours.py

@@ -64,8 +64,13 @@ class Images2Neibs(Op):
     def grad(self, inp, grads):
         x, neib_shape, neib_step = inp
         gz, = grads
-        if self.mode in ['valid','ignore_borders']:
-            return [neibs2images(gz, neib_shape, x.shape, mode=self.mode), None, None]
+        if self.mode in ['valid', 'ignore_borders']:
+            raise Exception("The Images2Neibs grad is not implemented."
+                            " It was in the past, but returned the wrong"
+                            " answer!")
+            # This is the reverse of the op, not the grad!
+            return [neibs2images(gz, neib_shape, x.shape, mode=self.mode),
+                    None, None]
         else:
             raise NotImplementedError()
 

theano/sandbox/test_neighbours.py

@@ -361,56 +361,8 @@ def speed_neibs_wrap_centered():
     for i in range(1000):
         f()
 
-
-def test_neibs_grad():
-    shape = (2, 3, 4, 4)
-    images = shared(numpy.arange(numpy.prod(shape),
-                                 dtype='float32').reshape(shape))
-
-    cost = T.sum(T.sqr(images2neibs(images, (2, 2))), axis=[0, 1])
-
-    grad = T.grad(cost, images)
-
-    f = theano.function([], [cost, grad], mode=mode_without_gpu)
-
-    got = f()
-
-    should_get = [numpy.asarray(290320.0, dtype=numpy.float32),
-        numpy.asarray([[[[  0.,   2.,   4.,   6.],
-                         [  8.,  10.,  12.,  14.],
-                         [ 16.,  18.,  20.,  22.],
-                         [ 24.,  26.,  28.,  30.]],
-
-                        [[ 32.,  34.,  36.,  38.],
-                         [ 40.,  42.,  44.,  46.],
-                         [ 48.,  50.,  52.,  54.],
-                         [ 56.,  58.,  60.,  62.]],
-
-                        [[ 64.,  66.,  68.,  70.],
-                         [ 72.,  74.,  76.,  78.],
-                         [ 80.,  82.,  84.,  86.],
-                         [ 88.,  90.,  92.,  94.]]],
-
-                       [[[ 96.,  98., 100., 102.],
-                         [104., 106., 108., 110.],
-                         [112., 114., 116., 118.],
-                         [120., 122., 124., 126.]],
-
-                        [[128., 130., 132., 134.],
-                         [136., 138., 140., 142.],
-                         [144., 146., 148., 150.],
-                         [152., 154., 156., 158.]],
-
-                        [[160., 162., 164., 166.],
-                         [168., 170., 172., 174.],
-                         [176., 178., 180., 182.],
-                         [184., 186., 188., 190.]]]], dtype=numpy.float32)]
-
-    assert numpy.allclose(got[0], should_get[0])
-    assert numpy.allclose(got[1], should_get[1])
-
-
-def test_neibs_grad_verify_grad():
+# Disable the test as the grad is wronly implemented
+def tes_neibs_grad_verify_grad():
     shape = (2, 3, 4, 4)
     images = T.dtensor4()
     images_val = numpy.arange(numpy.prod(shape),
@@ -458,7 +410,8 @@ def test_neibs_ignore_border():
         return T.sum(T.sqr(images2neibs(images, (2, 2),
                                         mode='ignore_borders')), axis=[0, 1])
 
-    unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu)
+    # Disable the test as the grad is wronly implemented
+    #unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu)
 
 #    not implemented for gpu
 #    if cuda.cuda_available:
@@ -475,15 +428,18 @@ def test_neibs_valid_with_inconsistent_borders():
         return T.sum(T.sqr(images2neibs(images, (2, 2), mode='valid')),
                      axis=[0, 1])
 
+    f = theano.function([images],
+                        T.sqr(images2neibs(images, (2, 2), mode='valid')))
     try:
-        unittest_tools.verify_grad(fn, [images_val], mode=mode_without_gpu)
+        f(images_val)
         assert False, "An error was expected"
-    except TypeError:
+    except TypeError, e:
         # This is expected if the assert is there
         pass
 
 
-def test_neibs2images_crash_on_grad():
+# Disable the test as the grad is wronly implemented
+def tes_neibs2images_crash_on_grad():
     # say we had images of size (2, 3, 20, 20)
     # then we extracted 2x2 neighbors on this, we get (2 * 3 * 10 * 10, 4)
     neibs = T.dmatrix()