added tests

theano/sandbox/cuda/tests/test_dnn.py

+from nose.plugins.skip import SkipTest
+import numpy
+import unittest
+
+import theano
+from theano.gof.python25 import any
+import theano.tensor as T
+import theano.tests.unittest_tools as utt
+
+# Skip test if cuda_ndarray is not available.
+import theano.sandbox.cuda as cuda
+if cuda.cuda_available == False:
+    raise SkipTest('Optional package cuda disabled')
+
+if theano.config.mode == 'FAST_COMPILE':
+    mode_with_gpu = theano.compile.mode.get_mode('FAST_RUN').including('gpu')
+    mode_without_gpu = theano.compile.mode.get_mode(
+        'FAST_RUN').excluding('gpu')
+else:
+    mode_with_gpu = theano.compile.mode.get_default_mode().including('gpu')
+    mode_without_gpu = theano.compile.mode.get_default_mode().excluding('gpu')
+
+
+def test_pooling():
+    if not cuda.dnn.dnn_available():
+        raise SkipTest(cuda.dnn.dnn_available.msg)
+    
+    x = T.tensor4()
+
+    out1 = cuda.dnn.dnn_pool(x, ws=(5,5), stride=(5, 5))
+    out2 = T.signal.downsample.max_pool_2d(x, (5, 5))
+
+    f1 = theano.function([x], out1)
+    f2 = theano.function([x], out2)
+
+    data = numpy.random.normal(0, 1, (1, 10, 100, 100)).astype("float32")
+    a = f1(data).__array__()
+
+    b = f2(data).__array__()
+
+    assert numpy.allclose(a, b)
+
+    gf1 = theano.function([x], theano.grad(out1.sum(), x))
+    gf2 = theano.function([x], theano.grad(out2.sum(), x))
+
+    ga = gf1(data).__array__()
+
+    gb = gf2(data).__array__()
+
+    assert numpy.allclose(ga, gb)