Add test for the previous fix and add more check to prevent other crash with bad…

theano/tensor/nnet/neighbours.py

@@ -152,7 +152,7 @@ class Images2Neibs(Op):
                 grad_undefined(self, 2, neib_step)]
 
     def c_code_cache_version(self):
-        return (5,)
+        return (6,)
 
     def perform(self, node, inp, out_):
         ten4, neib_shape, neib_step = inp
@@ -317,8 +317,24 @@ class Images2Neibs(Op):
         const npy_intp c = (npy_intp) *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 0);
         const npy_intp d = (npy_intp) *(dtype_%(neib_shape)s*) PyArray_GETPTR1(%(neib_shape)s, 1);
         // (step_x,step_y) = neib_step
-        const npy_intp step_x = (npy_intp) *(dtype_%(neib_step)s*) PyArray_GETPTR1(%(neib_step)s, 0);
-        const npy_intp step_y = (npy_intp) *(dtype_%(neib_step)s*) PyArray_GETPTR1(%(neib_step)s, 1);
+        const dtype_%(neib_step)s step_x = *(dtype_%(neib_step)s*) PyArray_GETPTR1(%(neib_step)s, 0);
+        const dtype_%(neib_step)s step_y = *(dtype_%(neib_step)s*) PyArray_GETPTR1(%(neib_step)s, 1);
+
+        if (step_x <=0 || step_y <=0)
+        {
+            PyErr_Format(PyExc_ValueError,
+                         "neib_step wrong step ; values <= 0. Got %%d %%d.",
+                         step_x, step_y);
+            %(fail)s;
+        }
+
+        if (c <=0 || d <=0)
+        {
+            PyErr_Format(PyExc_ValueError,
+                         "neib_shape values <= 0. Got %%d %%d.",
+                         c, d);
+            %(fail)s;
+        }
 
         if ( "%(mode)s" == "wrap_centered") {
             if (c%%2!=1 || d%%2!=1){

theano/tensor/nnet/tests/test_neighbours.py

@@ -340,6 +340,31 @@ class T_Images2Neibs(unittest_tools.InferShapeTester):
                             mode=self.mode)
         self.assertRaises(TypeError, f, images_val)
 
+    def test_can_not_infer_nb_dim(self):
+        # Was reported in gh-5613. Test that we do not crash
+        # or that we crash in a few other case found while
+        # investigating that case
+
+        img = T.tensor4('img')
+        patches = T.nnet.neighbours.images2neibs(img, [16, 16])
+        extractPatches = theano.function([img], patches)
+
+        patsRecovery = T.matrix('patsRecovery')
+        original_size = T.ivector('original_size')
+
+        out = neibs2images(patsRecovery, (16, 16), original_size)
+        f = theano.function([patsRecovery, original_size], out)
+
+        im_val = numpy.ones((1, 3, 320, 320), dtype=numpy.float32)
+        neibs = extractPatches(im_val)
+        f(neibs, im_val.shape)
+        # Wrong number of dimensions
+        self.assertRaises(ValueError, f, neibs,
+                          (1, 1, 3, 320, 320))
+        # End up with a step of 0
+        self.assertRaises(ValueError, f, neibs,
+                          (3, 320, 320, 1))
+
     def speed_neibs(self):
         shape = (100, 40, 18, 18)
         images = shared(numpy.arange(numpy.prod(shape),