Add tests for DownsampleFactorMaxGrad average+sum

theano/tensor/signal/tests/test_downsample.py

@@ -316,6 +316,9 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
         maxpoolsizes = ((5, 3), (3, 5), (3, 3))
         stridesizes = ((3, 2), (2, 3), (3, 3))
         paddingsizes = ((2, 2), (2, 1), (2, 2))
+        # average_inc_pad and average_exc_pad do not
+        # support grad with padding
+        for mode in ['max', 'sum']:
             for i in range(len(imgsizes)):
                 imgsize = imgsizes[i]
                 imval = rng.rand(1, 1, imgsize[0], imgsize[1]) * 10.0
@@ -328,6 +331,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                         maxpoolsize, ignore_border=True,
                         st=stridesize,
                         padding=paddingsize,
+                        mode=mode,
                         )(input)
                 utt.verify_grad(mp, [imval], rng=rng)
 
@@ -337,13 +341,16 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
         imval = rng.rand(2, 3, 3, 4) * 10.0
         # more variance means numeric gradient will be more accurate
 
-        for maxpoolshp in maxpoolshps:
-            for ignore_border in [True, False]:
-                # print 'maxpoolshp =', maxpoolshp
-                # print 'ignore_border =', ignore_border
+        for maxpoolshp, ignore_border, mode in product(maxpoolshps,
+                                                       [True, False],
+                                                       ['max',
+                                                        'sum',
+                                                        'average_inc_pad',
+                                                        'average_exc_pad']):
             def mp(input):
                 return DownsampleFactorMax(maxpoolshp,
-                                               ignore_border=ignore_border)(input)
+                                           ignore_border=ignore_border,
+                                           mode=mode)(input)
             utt.verify_grad(mp, [imval], rng=rng)
 
     def test_DownsampleFactorMax_grad_st(self):
@@ -353,13 +360,17 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
         stridesizes = ((1, 1), (3, 3), (5, 7))
         imval = rng.rand(1, 2, 16, 16)
 
-        for maxpoolshp in maxpoolshps:
-            for ignore_border in [True, False]:
-                for stride in stridesizes:
+        for maxpoolshp, ignore_border, mode, stride in product(maxpoolshps,
+                                                       [True, False],
+                                                       ['max',
+                                                        'sum',
+                                                        'average_inc_pad',
+                                                        'average_exc_pad'],
+                                                       stridesizes):
             def mp(input):
                 return DownsampleFactorMax(maxpoolshp,
                                            ignore_border=ignore_border,
-                                                   st=stride)(input)
+                                           st=stride, mode=mode)(input)
             utt.verify_grad(mp, [imval], rng=rng)
 
     def test_DownsampleFactorMax_grad_st_extra(self):
@@ -372,6 +383,7 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
         imvsizs = ((16, 16), (16, 16), (16, 16), (8, 5),
                    (8, 5), (8, 5), (8, 5))
 
+        for mode in ['max', 'sum', 'average_inc_pad', 'average_exc_pad']:
             for indx in numpy.arange(len(maxpoolshps)):
                 imvsize = imvsizs[indx]
                 imval = rng.rand(1, 2, imvsize[0], imvsize[1])
@@ -381,7 +393,8 @@ class TestDownsampleFactorMax(utt.InferShapeTester):
                     def mp(input):
                         return DownsampleFactorMax(maxpoolshp,
                                                    ignore_border=ignore_border,
-                                               st=stride)(input)
+                                                   st=stride,
+                                                   mode=mode)(input)
                     utt.verify_grad(mp, [imval], rng=rng)
 
     def test_DownsampleFactorMaxGrad_grad(self):