Add pool grad grad tests

theano/gpuarray/tests/test_blas.py

@@ -2,21 +2,26 @@ from __future__ import absolute_import, print_function, division
 from unittest import TestCase
 from nose.plugins.skip import SkipTest
 import itertools
+import copy
+
+import numpy
 
 import theano
+from theano import gradient
 from theano import tensor
 from theano.tests import unittest_tools as utt
 from theano.tensor.blas import gemv_inplace, gemm_inplace, _dot22, batched_dot
 from theano.tensor.tests.test_blas import TestGer, BaseGemv
+from theano.tensor.signal.pool import Pool, DownsampleFactorMaxGradGrad
 
 from .. import gpuarray_shared_constructor
-from .config import mode_with_gpu
+from .config import mode_with_gpu, mode_without_gpu
 from .test_basic_ops import makeTester, rand
 
 from ..blas import (gpugemv_inplace, gpugemv_no_inplace,
                     gpugemm_inplace, gpugemmbatch_no_inplace,
                     gpuger_inplace, gpuger_no_inplace,
-                    GpuGer, gpu_dot22)
+                    GpuGer, gpu_dot22, GpuDownsampleFactorMaxGradGrad)
 
 
 GpuGemvTester = makeTester(
@@ -128,3 +133,71 @@ GpuDot22Tester = makeTester(
         # test9=[rand(0, 0), rand(0, 0)],
     )
 )
+
+
+def test_downsample_max_grad_grad():
+    shps = [(1, 12),
+            (1, 1, 12),
+            (1, 1, 1, 12),
+            (1, 1, 2, 2),
+            (1, 1, 1, 1),
+            (1, 1, 4, 4),
+            (1, 1, 10, 11),
+            (1, 2, 2, 2),
+            (3, 5, 4, 4),
+            (25, 1, 7, 7),
+            (1, 1, 12, 12),
+            (1, 1, 2, 14),
+            (1, 1, 12, 14),
+            (1, 1, 14, 14),
+            (1, 1, 16, 16),
+            (1, 1, 18, 18),
+            (1, 1, 24, 24),
+            (1, 6, 24, 24),
+            (10, 1, 24, 24),
+            (10, 6, 24, 24),
+            (30, 6, 12, 12),
+            (30, 2, 24, 24),
+            (30, 6, 24, 24),
+            (10, 10, 10, 11),
+            (1, 1, 10, 1025),
+            (1, 1, 10, 1023),
+            (1, 1, 1025, 10),
+            (1, 1, 1023, 10), ]
+
+    numpy.random.RandomState(utt.fetch_seed()).shuffle(shps)
+    test_ds = (2, 2), (3, 2), (1, 1)
+    test_st = (2, 2), (3, 2), (1, 1)
+
+    for shp in shps:
+        for ds, st in itertools.product(test_ds, test_st):
+            if ds[0] > shp[-2]:
+                continue
+            if ds[1] > shp[-1]:
+                continue
+            for ignore_border in (True, False):
+                # print 'test_downsample', shp, ds, st, ignore_border
+                ds_op = Pool(ndim=len(ds), ignore_border=ignore_border)
+
+                a = theano.shared(rand(*shp), 'a')
+
+                ggf = gradient.Lop(tensor.grad((ds_op(
+                    tensor.as_tensor_variable(a), ds, st)**2).sum(), a), a, a)
+
+                ref_mode = copy.copy(mode_without_gpu)
+                ref_mode.check_py_code = False
+                gpu_mode = copy.copy(mode_with_gpu)
+                gpu_mode.check_py_code = False
+                gg = theano.function([], ggf, mode=gpu_mode)
+                gg2 = theano.function([], ggf, mode=ref_mode)
+
+                assert any([
+                    isinstance(node.op, GpuDownsampleFactorMaxGradGrad)
+                    for node in gg.maker.fgraph.toposort()
+                ])
+                assert any([
+                    isinstance(node.op, DownsampleFactorMaxGradGrad)
+                    for node in gg2.maker.fgraph.toposort()
+                ])
+                assert numpy.allclose(gg(), gg2()), (shp, ds, st,
+                                                     ignore_border)