testing infer_shape: op DownSampleFactorMaxGrad

theano/tensor/signal/tests/test_downsample.py

@@ -2,7 +2,8 @@ import unittest, sys, time
 import numpy
 import theano.tensor as tensor
 from theano.tests import unittest_tools as utt
-from theano.tensor.signal.downsample import DownsampleFactorMax, max_pool_2d
+from theano.tensor.signal.downsample import (DownsampleFactorMax, max_pool_2d,
+                                             DownsampleFactorMaxGrad)
 from theano import function, Mode
 
 
@@ -158,21 +159,51 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
 
     def test_infer_shape(self):
 
-        ## TODO: maxpoolshp != (1, 1) fails with ignore_border == False
-        # see function out_shape in class DownsampleFactorMax
-
-        images = tensor.dtensor4()
+        image = tensor.dtensor4()
+        maxout = tensor.dtensor4()
+        gz = tensor.dtensor4()
         rng = numpy.random.RandomState(utt.fetch_seed())
         maxpoolshps = ((1, 1), (2, 2), (3, 3), (2, 3), (3, 2))
-        imval = rng.rand(4, 10, 64, 64)
-        # imval = rng.rand(2, 3, 3, 4)
-        for maxpoolshp in maxpoolshps:
-            for ignore_border in [True, False]:
-                print maxpoolshp, ignore_border
-                self._compile_and_check([images],
+        image_val = rng.rand(2, 3, 3, 4)
+        out_shapes = [[[2, 3, 3, 4], [2, 3, 3, 4]],
+                       [[2, 3, 1, 2], [2, 3, 2, 2]],
+                       [[2, 3, 1, 1], [2, 3, 1, 2]],
+                       [[2, 3, 1, 1], [2, 3, 2, 2]],
+                       [[2, 3, 1, 2], [2, 3, 1, 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]]]
+        image_val = rng.rand(4, 10, 64, 64)
+        out_shapes = [[[4, 10, 64, 64], [4, 10, 64, 64]],
+                      [[4, 10, 32, 32], [4, 10, 32, 32]],
+                      [[4, 10, 21, 21], [4, 10, 22, 22]],
+                      [[4, 10, 32, 21], [4, 10, 32, 22]],
+                      [[4, 10, 21, 32], [4, 10, 22, 32]]]
+
+        """
+
+        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)(images)],
-                        [imval], DownsampleFactorMax)
+                        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)
 
 
 """