def _setup(self, device):
y_shape = self.y_shape
x_shape = self.x_shape
y1 = make_array(1, y_shape, numpy.float32, device)
y2 = make_array(2, y_shape, numpy.float32, device)
gx1 = chainer.Variable(make_array(1, x_shape, numpy.float32, device))
gx2 = None
gy1 = make_array(1, y_shape, numpy.float32, device)
gy2 = make_array(1, y_shape, numpy.float32, device)
f = chainer.FunctionNode()
f.check_type_forward = mock.MagicMock()
f.forward_cpu = mock.MagicMock()
f.forward_gpu = mock.MagicMock()
f.backward = mock.MagicMock(return_value=(gx1, gx2))
self.f = f
self.x1 = make_array(0, x_shape, numpy.float32, device)
self.x2 = make_array(0, x_shape, numpy.int32, device)
self.y1 = y1
self.y2 = y2
self.gx1 = gx1
self.gx2 = gx2
self.gx1_orig = chainer.Variable(
make_array(3, x_shape, numpy.float32, device))
self.gx2_orig = chainer.Variable(
make_array(2, x_shape, numpy.float32, device))
self.gx1_accum = gx1 + self.gx1_orig
self.gy1 = gy1
self.gy2 = gy2
def setUp(self):
self.original_debug = chainer.is_debug()
chainer.set_debug(True)
self.one = numpy.array(1, numpy.float32)
self.f = chainer.FunctionNode()
self.return_value = tuple(None if x is None else chainer.Variable(x)
for x in self.return_data)
def test_set_fresh_creator_node(self):
v = chainer.Variable()
f = chainer.FunctionNode()
v.creator_node = f
self.assertIs(v.creator, f)
self.assertIs(v.creator_node, f)
self.assertEqual(v.rank, 1)
def setUp(self):
y1 = numpy.arange(4).astype(numpy.float32)
y2 = numpy.arange(4).astype(numpy.float32) + 1
gx1 = chainer.Variable(numpy.arange(3).astype(numpy.float32))
gx2 = None
gy1 = numpy.arange(4).astype(numpy.float32)
gy2 = numpy.arange(4).astype(numpy.float32)
f = chainer.FunctionNode()
f.check_type_forward = mock.MagicMock()
f.forward_cpu = mock.MagicMock(return_value=(y1, y2))
f.forward_gpu = mock.MagicMock()
f.backward = mock.MagicMock(return_value=(gx1, gx2))
self.f = f
self.x1 = numpy.arange(3).astype(numpy.float32)
self.x2 = numpy.arange(3).astype(numpy.int32)
self.y1 = y1
self.y2 = y2
self.gx1 = gx1
self.gx2 = gx2
self.gx1_orig = chainer.Variable(
numpy.arange(3, 6).astype(numpy.float32))
self.gx2_orig = chainer.Variable(
numpy.arange(2, 5).astype(numpy.float32))
self.gx1_accum = gx1 + self.gx1_orig
self.gy1 = gy1
self.gy2 = gy2
def check_zerograd(self, a_data, fill=False):
xp = cuda.get_array_module(a_data)
a = chainer.Variable(a_data)
if fill:
a.grad_var = chainer.Variable(xp.full_like(a_data, np.nan))
a.grad_var.creator_node = chainer.FunctionNode()
a.zerograd()
self.assertIsNot(a.grad, None)
if fill:
self.assertIsNone(a.grad_var.creator_node)
g_expect = xp.zeros_like(a.data)
xp.testing.assert_array_equal(a.grad, g_expect)
def test_multiple_output_call_count(self):
x = chainer.Variable(np.array([1, 2], np.float32))
f = chainer.FunctionNode()
f.forward = mock.MagicMock(
side_effect=lambda xs: tuple(x * 2 for x in xs))
f.backward = mock.MagicMock(
side_effect=lambda _, gys: tuple(gy * 2 for gy in gys))
h, = f.apply((x,))
y0 = h * 3
y1 = h * 4
y0.grad = np.array([1, 10], np.float32)
y1.grad = np.array([100, 1000], np.float32)
chainer.backward([y0, y1])
testing.assert_allclose(x.grad, np.array([806, 8060], np.float32))
assert f.backward.call_count == 1
def setUp(self):
self.f = chainer.FunctionNode()
def setUp(self):
self.original_debug = chainer.is_debug()
chainer.set_debug(True)
self.one = numpy.array([1], numpy.float32)
self.f = chainer.FunctionNode()
