def test_tensor_pow(self):
x = tF.input(self.a)
y = tF.input(self.b)
self.assertTrue(
np.isclose((x**y).to_ndarrays()[0], np.array([[1, 2],
[81, 65536]])).all())
self.assertTrue(
np.isclose((x**2).to_ndarrays()[0], np.array([[1, 4],
[9, 16]])).all())
self.assertTrue(
np.isclose((2**x).to_ndarrays()[0], np.array([[2, 4],
[8, 16]])).all())
self.assertTrue(
np.isclose((x**-2).to_ndarrays()[0],
np.array([[1, 1 / 4], [1 / 9, 1 / 16]])).all())
input_arr = np.array([1, -1, 3, -3, 5, -5])
x = tF.input(input_arr)
self.assertTrue(((x**6).to_ndarrays()[0] == np.array(
[1, 1, 729, 729, 15625, 15625])).all())
self.assertTrue(((x**9).to_ndarrays()[0] == np.array(
[1, -1, 19683, -19683, 1953125, -1953125])).all())
input_arr = np.array([1, -1])
x = tF.input(input_arr)
self.assertTrue(
((x**0x7fffffff).to_ndarrays()[0] == np.array([1, -1])).all())
self.assertTrue(
((x**-0x80000000).to_ndarrays()[0] == np.array([1, 1])).all())
self.assertTrue(np.isnan((x**0x80000000).to_ndarrays()[0]).any())
self.assertTrue(np.isnan((x**-0x80000001).to_ndarrays()[0]).any())
self.assertRaises(TypeError, lambda: pow(x, y, 2))
def test_tensor_matmul(self):
x = tF.input(self.a)
y = tF.input(self.b)
self.assertTrue(
((x @ y).to_ndarrays()[0] == np.array([[9, 17], [19, 35]])).all())
self.assertRaises(TypeError, lambda: x @ 2)
self.assertRaises(TypeError, lambda: 2 @ x)
def test_tensor_iadd(self):
x = tF.input(self.a)
y = tF.input(self.b)
x_tmp = x
x += y
self.assertIs(x, x_tmp)
self.assertTrue((x.to_ndarrays()[0] == np.array([[2, 3], [7,
12]])).all())
def test_tensor_isub(self):
x = tF.input(self.a)
y = tF.input(self.b)
x_tmp = x
x -= y
self.assertIs(x, x_tmp)
self.assertTrue((x.to_ndarrays()[0] == np.array([[0, 1], [-1,
-4]])).all())
def test_tensor_mul(self):
x = tF.input(self.a)
y = tF.input(self.b)
self.assertTrue(
((x * y).to_ndarrays()[0] == np.array([[1, 2], [12, 32]])).all())
self.assertTrue(((x * 2).to_ndarrays()[0] == np.array([[2, 4],
[6, 8]])).all())
self.assertTrue(((2 * x).to_ndarrays()[0] == np.array([[2, 4],
[6, 8]])).all())
def test_tensor_sub(self):
x = tF.input(self.a)
y = tF.input(self.b)
self.assertTrue(
((x - y).to_ndarrays()[0] == np.array([[0, 1], [-1, -4]])).all())
self.assertTrue(((x - 2).to_ndarrays()[0] == np.array([[-1, 0],
[1, 2]])).all())
self.assertTrue(
((2 - x).to_ndarrays()[0] == np.array([[1, 0], [-1, -2]])).all())
def test_tensor_add(self):
x = tF.input(self.a)
y = tF.input(self.b)
self.assertTrue(
((x + y).to_ndarrays()[0] == np.array([[2, 3], [7, 12]])).all())
self.assertTrue(((x + 2).to_ndarrays()[0] == np.array([[3, 4],
[5, 6]])).all())
self.assertTrue(((2 + x).to_ndarrays()[0] == np.array([[3, 4],
[5, 6]])).all())
def test_tensor_truediv(self):
x = tF.input(self.a)
y = tF.input(self.b)
self.assertTrue(((x / y).to_ndarrays()[0] == np.array([[1, 2],
[0.75,
0.5]])).all())
self.assertTrue(((x / 2).to_ndarrays()[0] == np.array([[0.5, 1],
[1.5,
2]])).all())
self.assertTrue(((2 / y).to_ndarrays()[0] == np.array([[2, 2],
[0.5,
0.25]])).all())
def test_model_load_save(self):
submodel = TestModel()
submodel.sp1 = Parameter([2, 4], I.Constant(0))
submodel.sp1.value = tF.input(np.array([[0, 1, 2, 3], [4, 5, 6, 7]]))
submodel.sp2 = Parameter([2, 4], I.Constant(0))
submodel.sp2.value = tF.input(np.array([[9, 8, 7, 6], [5, 4, 3, 2]]))
submodel.add("sp1", submodel.sp1)
submodel.add("sp2", submodel.sp2)
parentmodel = TestModel()
parentmodel.p1 = Parameter([4, 2], I.Constant(0))
parentmodel.p1.value = tF.input(
np.array([[0, 1], [2, 3], [4, 5], [6, 7]]))
parentmodel.p2 = Parameter([4, 2], I.Constant(0))
parentmodel.p2.value = tF.input(
np.array([[9, 8], [7, 6], [5, 4], [3, 2]]))
parentmodel.sub = submodel
parentmodel.add("p1", parentmodel.p1)
parentmodel.add("p2", parentmodel.p2)
parentmodel.add("sub", parentmodel.sub)
submodel_load = TestModel()
submodel_load.sp1 = Parameter()
submodel_load.sp2 = Parameter()
submodel_load.add("sp1", submodel_load.sp1)
submodel_load.add("sp2", submodel_load.sp2)
parentmodel_load = TestModel()
parentmodel_load.p1 = Parameter()
parentmodel_load.p2 = Parameter()
parentmodel_load.sub = submodel_load
parentmodel_load.add("p1", parentmodel_load.p1)
parentmodel_load.add("p2", parentmodel_load.p2)
parentmodel_load.add("sub", parentmodel_load.sub)
with tempfile.NamedTemporaryFile() as fp:
parentmodel.save(fp.name)
parentmodel_load.load(fp.name)
self.assertTrue(
(parentmodel_load.p1.value.to_ndarrays()[0] == np.array([[0, 1],
[2, 3],
[4, 5],
[6, 7]
])).all())
self.assertTrue(
(parentmodel_load.p2.value.to_ndarrays()[0] == np.array([[9, 8],
[7, 6],
[5, 4],
[3, 2]
])).all())
self.assertTrue(
(parentmodel_load.sub.sp1.value.to_ndarrays()[0] == np.array(
[[0, 1, 2, 3], [4, 5, 6, 7]])).all())
self.assertTrue(
(parentmodel_load.sub.sp2.value.to_ndarrays()[0] == np.array(
[[9, 8, 7, 6], [5, 4, 3, 2]])).all())
def test_tensor_imul(self):
x = tF.input(self.a)
x_tmp = x
x *= 2
self.assertIs(x, x_tmp)
self.assertTrue((x.to_ndarrays()[0] == np.array([[2, 4], [6,
8]])).all())
def test_device_instance(self):
dev = Device.get_default()
self.assertIs(dev, self.device)
tensor = tF.input([0], Shape([]))
dev = tensor.device()
self.assertIs(dev, self.device)
node = F.input([0], Shape([]))
dev = node.device()
self.assertIs(dev, self.device)
my_device = Naive()
self.assertIsNot(my_device, self.device)
node = F.input([0], Shape([]), device=my_device)
dev = node.device()
self.assertIs(dev, my_device)
dev = self.graph.get_device(node)
self.assertIs(dev, my_device)
param = Parameter(Shape([]))
dev = param.device()
self.assertIs(dev, self.device)
def test_parameter_gradient(self):
self.p.reset_gradient()
self.assertTrue((self.p.gradient.to_ndarrays() == np.zeros([8])).all())
grad = self.p.gradient
self.p.gradient += tF.input(np.ones([8]))
self.assertTrue((grad.to_ndarrays()[0] == np.ones([8])).all())
with self.assertRaises(NotImplementedError):
del self.p.gradient
def test_parameter_value(self):
self.assertTrue(
(self.p.value.to_ndarrays() == np.array([1, 2, 3, 4, 5, 6, 7,
8])).all())
val = self.p.value
self.p.value += tF.input(np.ones([8]))
self.assertTrue(
(val.to_ndarrays()[0] == np.array([2, 3, 4, 5, 6, 7, 8, 9])).all())
with self.assertRaises(NotImplementedError):
del self.p.value
def test_parameter_stats(self):
self.p.add_stats("stat1", Shape([2, 3]))
self.p.add_stats("stat2", Shape([2, 4]))
st1 = self.p.stats["stat1"]
st1.reset(0)
self.assertTrue((st1.to_ndarrays()[0] == np.zeros([2, 3])).all())
self.p.stats["stat1"] = tF.input(np.ones([2, 3]))
self.assertTrue((st1.to_ndarrays()[0] == np.ones([2, 3])).all())
self.assertIn("stat1", self.p.stats)
self.assertIn("stat2", self.p.stats)
self.assertNotIn("stat3", self.p.stats)
with self.assertRaises(NotImplementedError):
del self.p.stats["stat1"]
with self.assertRaises(AttributeError):
self.p.stats = _ParameterStatistics(self.p)
def test_tensor_neg(self):
x = tF.input(self.a)
y = tF.input(self.b)
self.assertTrue(((-x).to_ndarrays()[0] == -self.a).all())
def test_tensor_pos(self):
x = tF.input(self.a)
y = tF.input(self.b)
self.assertTrue(((+x).to_ndarrays()[0] == self.a).all())