changing test_infer_shape in TestDownsampleFactorMax to include padding

theano/tensor/signal/tests/test_downsample.py

@@ -622,31 +622,45 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
         maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3), (3, 2))
 
         image_val = rng.rand(4, 6, 7, 9)
-        out_shapes = [[[4, 6, 7, 9], [4, 6, 7, 9]],
-                      [[4, 6, 3, 4], [4, 6, 4, 5]],
-                      [[4, 6, 2, 3], [4, 6, 3, 3]],
-                      [[4, 6, 3, 3], [4, 6, 4, 3]],
-                      [[4, 6, 2, 4], [4, 6, 3, 5]]]
+        out_shapes = [[[[4, 6, 7, 9], [4, 6, 7, 9]],
+                       [[4, 6, 3, 4], [4, 6, 4, 5]],
+                       [[4, 6, 2, 3], [4, 6, 3, 3]],
+                       [[4, 6, 3, 3], [4, 6, 4, 3]],
+                       [[4, 6, 2, 4], [4, 6, 3, 5]]],
+                      [[None, None],
+                       [[4, 6, 4, 5], None],
+                       [[4, 6, 3, 3], None],
+                       [[4, 6, 4, 3], None],
+                       [[4, 6, 3, 5], None]],
+                      [[None, None],
+                       [None, None],
+                       [[4, 6, 3, 4], None],
+                       [[4, 6, 4, 4], None],
+                       [None, None]]]
 
         for i, maxpoolshp in enumerate(maxpoolshps):
             for j, ignore_border in enumerate([True, False]):
-
-                # checking shapes generated by DownsampleFactorMax
-                self._compile_and_check([image],
-                                        [DownsampleFactorMax(maxpoolshp,
-                                        ignore_border=ignore_border)(image)],
-                                        [image_val], DownsampleFactorMax)
-
-                # checking shapes generated by DownsampleFactorMaxGrad
-                maxout_val = rng.rand(*out_shapes[i][j])
-                gz_val = rng.rand(*out_shapes[i][j])
-                self._compile_and_check([image, maxout, gz],
-                                        [DownsampleFactorMaxGrad(maxpoolshp,
-                                        ignore_border=ignore_border)
-                                        (image, maxout, gz)],
-                                        [image_val, maxout_val, gz_val],
-                                        DownsampleFactorMaxGrad,
-                                        warn=False)
+                for k, padding in enumerate([(0,0), (1,1), (1,2)]):
+                    if out_shapes[k][i][j] == None:
+                        continue
+                    # checking shapes generated by DownsampleFactorMax
+                    self._compile_and_check([image],
+                                            [DownsampleFactorMax(maxpoolshp,
+                                            ignore_border=ignore_border,
+                                            padding=padding)(image)],
+                                            [image_val], DownsampleFactorMax)
+
+                    # checking shapes generated by DownsampleFactorMaxGrad
+                    maxout_val = rng.rand(*out_shapes[k][i][j])
+                    gz_val = rng.rand(*out_shapes[k][i][j])
+                    self._compile_and_check([image, maxout, gz],
+                                            [DownsampleFactorMaxGrad(maxpoolshp,
+                                            ignore_border=ignore_border,
+                                            padding=padding)
+                                            (image, maxout, gz)],
+                                            [image_val, maxout_val, gz_val],
+                                            DownsampleFactorMaxGrad,
+                                            warn=False)
 
 
 if __name__ == '__main__':