Fix gh-4534. If ds size for pool is equal to image size and ignore_border=False,…

theano/tensor/signal/pool.py

@@ -278,6 +278,9 @@ class Pool(Op):
                 'Pool requires 4D input for now')
         z_shape = self.out_shape(x.shape, self.ds, self.ignore_border, self.st,
                                  self.padding)
+        if not self.ignore_border:
+            assert z_shape[2] > 0
+            assert z_shape[3] > 0
         if (z[0] is None) or (z[0].shape != z_shape):
             z[0] = numpy.empty(z_shape, dtype=x.dtype)
         zz = z[0]
@@ -407,7 +410,7 @@ class Pool(Op):
             }
             else
             {
-                z_r = std::max(0, (r - 1 - %(ds0)s) / %(st0)s + 1) + 1;
+                z_r = std::max(0, (r - 1 - %(ds0)s + %(st0)s) / %(st0)s) + 1;
             }
             // decide how many columns the output has
             if (%(st1)s >= %(ds1)s)
@@ -416,8 +419,10 @@ class Pool(Op):
             }
             else
             {
-                z_c = std::max(0, (c - 1 - %(ds1)s) / %(st1)s + 1) + 1;
+                z_c = std::max(0, (c - 1 - %(ds1)s + %(st0)s) / %(st1)s) + 1;
             }
+            assert(z_r > 0);
+            assert(z_c > 0);
         }
         // memory allocation of z if necessary
         if ((!%(z)s)
@@ -526,7 +531,7 @@ class Pool(Op):
         return ccode % locals()
 
     def c_code_cache_version(self):
-        return (0, 6, 8, 3)
+        return (0, 6, 8, 4)
 
 
 class PoolGrad(Op):

theano/tensor/signal/tests/test_pool.py

@@ -208,17 +208,21 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
 
     def test_DownsampleFactorMaxStride(self):
         rng = numpy.random.RandomState(utt.fetch_seed())
-        maxpoolshps = ((1, 1), (3, 3), (5, 3))
-        stridesizes = ((1, 1), (3, 3), (5, 7))
+        maxpoolshps = ((1, 1), (3, 3), (5, 3), (16, 16))
+        stridesizes = ((1, 1), (3, 3), (5, 7),)
         # generate random images
         imval = rng.rand(4, 10, 16, 16)
         # The same for each mode
-        outputshps = ((4, 10, 16, 16), (4, 10, 6, 6), (4, 10, 4, 3),
+        outputshps = (
+            (4, 10, 16, 16), (4, 10, 6, 6), (4, 10, 4, 3),
             (4, 10, 16, 16), (4, 10, 6, 6), (4, 10, 4, 3),
             (4, 10, 14, 14), (4, 10, 5, 5), (4, 10, 3, 2),
             (4, 10, 14, 14), (4, 10, 6, 6), (4, 10, 4, 3),
             (4, 10, 12, 14), (4, 10, 4, 5), (4, 10, 3, 2),
-                      (4, 10, 12, 14), (4, 10, 5, 6), (4, 10, 4, 3))
+            (4, 10, 12, 14), (4, 10, 5, 6), (4, 10, 4, 3),
+            (4, 10, 1, 1), (4, 10, 1, 1), (4, 10, 1, 1),
+            (4, 10, 1, 1), (4, 10, 1, 1), (4, 10, 1, 1),)
+
         images = tensor.dtensor4()
         indx = 0
         for mode, maxpoolshp, ignore_border in product(['max',