def setUp(self):
self.func = PairwiseSampling(3, [10, 5, 2, 5, 2],
CovarianceType.diagonal)
self.mean = numpy.random.uniform(-1, 1, (2, 3)).astype(numpy.float32)
self.cov = numpy.random.uniform(0.1, 10, (2, 3)).astype(numpy.float32)
self.t = numpy.array([0, 2]).astype(numpy.int32)
self.func.zero_grads()
self.gy = numpy.random.uniform(-1, 1, ()).astype(numpy.float32)
class TestPairwiseSampling2(unittest.TestCase):
'''
spherical covariance
'''
def setUp(self):
self.func = PairwiseSampling(3, [10, 5, 2, 5, 2],
CovarianceType.spherical)
self.mean = numpy.random.uniform(-1, 1, (2, 3)).astype(numpy.float32)
self.cov = numpy.random.uniform(0.1, 10, (2, 1)).astype(numpy.float32)
self.t = numpy.array([0, 2]).astype(numpy.int32)
self.func.zero_grads()
self.gy = numpy.random.uniform(-1, 1, ()).astype(numpy.float32)
def check_backward(self, mean_data, cov_data, t_data, y_grad):
m = chainer.Variable(mean_data)
c = chainer.Variable(cov_data)
t = chainer.Variable(t_data)
y = self.func(m, c, t)
y.grad = y_grad
y.backward()
func = y.creator
f = lambda: func.forward((m.data, c.data, t.data))
gm, gc, _, gM, gC = gradient_check.numerical_grad(f,
(m.data, c.data, t.data, func.M, func.C), (y.grad,))
gradient_check.assert_allclose(cuda.to_cpu(gm), cuda.to_cpu(m.grad),
atol=5.e-4)
gradient_check.assert_allclose(cuda.to_cpu(gc), cuda.to_cpu(c.grad),
atol=5.e-4)
gradient_check.assert_allclose(cuda.to_cpu(gM), cuda.to_cpu(func.gM),
atol=5.e-4)
gradient_check.assert_allclose(cuda.to_cpu(gC), cuda.to_cpu(func.gC),
atol=5.e-4)
@attr.gpu
@condition.retry(3)
def test_forward_gpu(self):
m = chainer.Variable(self.mean)
c = chainer.Variable(self.cov)
t = chainer.Variable(self.t)
self.func._make_samples(self.t)
y = self.func(m, c, t)
self.assertEqual(y.data.dtype, numpy.float32)
self.assertEqual(y.data.shape, ())
self.func.to_gpu()
y_g = self.func(chainer.Variable(cuda.to_gpu(self.mean)),
chainer.Variable(cuda.to_gpu(self.cov)),
chainer.Variable(cuda.to_gpu(self.t)))
self.assertEqual(y_g.data.dtype, numpy.float32)
self.assertEqual(y_g.data.shape, ())
gradient_check.assert_allclose(y.data, y_g.data, atol=1.e-4)
@condition.retry(3)
def test_backward_cpu(self):
self.check_backward(self.mean, self.cov, self.t, self.gy)
@attr.gpu
@condition.retry(3)
def test_backward_gpu(self):
self.func.to_gpu()
self.check_backward(cuda.to_gpu(self.mean),
cuda.to_gpu(self.cov),
cuda.to_gpu(self.t),
cuda.to_gpu(self.gy))
@attr.gpu
def test_to_cpu(self):
self.func.to_gpu()
self.assertTrue(self.func.sampler.use_gpu)
self.func.to_cpu()
self.assertFalse(self.func.sampler.use_gpu)
