def test_ModelTest_CheckSaveLoad_Insufficient(self):
shape = Shape([2, 2])
values1 = [1, 2, 3, 4]
values2 = [5, 6, 7, 8]
tmp = tempfile.NamedTemporaryFile()
m1 = Model()
m2 = Model()
p1 = Parameter(shape, I.Constant(0))
p1.value += tF.raw_input(shape, values1)
p2 = Parameter(shape, I.Constant(0))
p2.value += tF.raw_input(shape, values2)
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.save(tmp.name)
m1 = Model()
m2 = Model()
p1 = Parameter()
m1.add("p", p1)
m1.add("sm", m2)
with self.assertRaises(RuntimeError):
m1.load(tmp.name)
def test_ModelTest_CheckSaveLoad_Same(self):
shape = Shape([2, 2])
values1 = [1, 2, 3, 4]
values2 = [5, 6, 7, 8]
tmp = tempfile.NamedTemporaryFile()
m1 = Model()
m2 = Model()
p1 = Parameter(shape, I.Constant(0))
p1.value += tF.raw_input(shape, values1)
p2 = Parameter(shape, I.Constant(0))
p2.value += tF.raw_input(shape, values2)
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.save(tmp.name)
m1 = Model()
m2 = Model()
p1 = Parameter()
p2 = Parameter()
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.load(tmp.name)
self.assertTrue(p1.valid())
self.assertTrue(p2.valid())
self.assertEqual(shape, p1.shape())
self.assertEqual(shape, p2.shape())
self.assertEqual(values1, p1.value.to_list())
self.assertEqual(values2, p2.value.to_list())
def test_TensorTest_CheckInplaceSubtractN1(self):
for dev in TensorTest.devices:
a_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
b_data = [0, 1, 2, 3]
y_data = [1, 1, 1, 1, 5, 5, 5, 5, 9, 9, 9, 9]
a = tF.raw_input(Shape([2, 2], 3), a_data, dev)
b = tF.raw_input([2, 2], b_data, dev)
a -= b
self.assertEqual(y_data, a.to_list())
def test_TensorTest_CheckInplaceAdd1N(self):
for dev in TensorTest.devices:
a_data = [1, 2, 3, 4]
b_data = [0, -1, -2, -3, -3, -4, -5, -6, -6, -7, -8, -9]
y_data = [-8, -10, -12, -14]
a = tF.raw_input([2, 2], a_data, dev)
b = tF.raw_input(Shape([2, 2], 3), b_data, dev)
a += b
self.assertEqual(y_data, a.to_list())
def test_TensorTest_CheckInplaceSubtract1N(self):
for dev in TensorTest.devices:
a_data = [1, 2, 3, 4]
b_data = [0, 1, 2, 3, 3, 4, 5, 6, 6, 7, 8, 9]
y_data = [-8, -10, -12, -14]
a = tF.raw_input([2, 2], a_data, dev)
b = tF.raw_input(Shape([2, 2], 3), b_data, dev)
a -= b
self.assertEqual(y_data, a.to_list())
def test_TensorTest_InplaceMultiplyConst(self):
for dev in TensorTest.devices:
x_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
y_data = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24]
x = tF.raw_input(Shape([2, 2], 3), x_data, dev)
x *= 2
self.assertEqual(y_data, x.to_list())
for dev in TensorTest.devices:
x_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
y_data = [.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, 6]
x = tF.raw_input(Shape([2, 2], 3), x_data, dev)
x *= .5
self.assertEqual(y_data, x.to_list())
def test_TensorTest_CheckInvalidInplaceOps(self):
for dev in TensorTest.devices:
shapes = [
Shape(),
Shape([], 3),
Shape([2, 2], 2),
]
a = tF.raw_input(Shape([2, 2], 3), [0] * 12, dev)
for shape in shapes:
b = tF.raw_input(shape, [0] * shape.size(), dev)
with self.assertRaises(RuntimeError):
a += b
with self.assertRaises(RuntimeError):
a -= b
def test_TesnorTest_CheckResetValuesByConstant(self):
for dev in TensorTest.devices:
x = tF.raw_input(Shape([2, 2], 2), [42] * 8, dev)
self.assertEqual([42] * 8, x.to_list())
x = tF.raw_input(Shape([2, 2], 2), [0] * 8, dev)
x.reset(42)
self.assertEqual([42] * 8, x.to_list())
x = tF.raw_input(Shape([2, 2], 2), [123] * 8, dev)
copied = Tensor(x)
x.reset(42)
self.assertEqual([42] * 8, x.to_list())
self.assertEqual([123] * 8, copied.to_list())
def test_TensorTest_CheckArgMinLarge(self):
ns = [
1,
2,
3,
15,
16,
17,
255,
256,
257,
1023,
1024,
1025,
65535,
65536,
65537,
]
for dev in TensorTest.devices:
for n in ns:
data = list(range(n))
random.shuffle(data)
pos = data.index(0)
a = tF.raw_input([n], data, dev)
self.assertEqual(pos, a.argmin(0)[0])
def test_TensorTest_CheckArgMinDims(self):
data = [
3,
4,
5,
0,
1,
2,
6,
7,
8,
0,
-1,
-2,
-6,
-7,
-8,
-3,
-4,
-5,
]
expected = [
[0, 0, 0, 2, 2, 2],
[1, 1, 1, 1, 1, 1],
[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
]
for dev in TensorTest.devices:
a = tF.raw_input(Shape([3, 3], 2), data, dev)
for i, exp in enumerate(expected):
self.assertEqual(exp, a.argmin(i))
def test_TensorTest_CheckInplaceSubtract(self):
for dev in TensorTest.devices:
a_data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
b_data = [0, 1, 2, 3, 3, 4, 5, 6, 6, 7, 8, 9]
y_data = [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]
a = tF.raw_input(Shape([2, 2], 3), a_data, dev)
b = tF.raw_input(Shape([2, 2], 3), b_data, dev)
copied = Tensor(a)
ref_a = a
a -= b
self.assertEqual(y_data, a.to_list())
self.assertEqual(y_data, a.to_list())
self.assertEqual(a_data, copied.to_list())
self.assertIs(ref_a, a)
def test_TensorTest_CheckResetValuesByVector(self):
for dev in TensorTest.devices:
data = [1, 2, 3, 4, 5, 6, 7, 8]
x = tF.raw_input(Shape([2, 2], 2), data, dev)
self.assertEqual(data, x.to_list())
data = [1, 2, 3, 4, 5, 6, 7, 8]
x = tF.raw_input(Shape([2, 2], 2), [0] * 8, dev)
x.reset_by_vector(data)
self.assertEqual(data, x.to_list())
data = [1, 2, 3, 4, 5, 6, 7, 8]
x = tF.raw_input(Shape([2, 2], 2), [123] * 8, dev)
copied = Tensor(x)
x.reset_by_vector(data)
self.assertEqual(data, x.to_list())
self.assertEqual([123] * 8, copied.to_list())
def test_ModelTest_CheckSaveLoadWithStats(self):
shape = Shape([2, 2])
values1 = [1, 2, 3, 4]
values2 = [5, 6, 7, 8]
stats1 = [10, 20, 30, 40]
stats2 = [50, 60, 70, 80]
tmp = tempfile.NamedTemporaryFile()
m1 = Model()
m2 = Model()
p1 = Parameter(shape, I.Constant(0))
p1.value += tF.raw_input(shape, values1)
p2 = Parameter(shape, I.Constant(0))
p2.value += tF.raw_input(shape, values2)
p1.add_stats("a", shape)
p2.add_stats("b", shape)
p1.stats["a"].reset_by_vector(stats1);
p2.stats["b"].reset_by_vector(stats2);
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.save(tmp.name)
m1 = Model()
m2 = Model()
p1 = Parameter()
p2 = Parameter()
m1.add("p", p1)
m2.add("p", p2)
m1.add("sm", m2)
m1.load(tmp.name)
self.assertTrue(p1.valid())
self.assertTrue(p2.valid())
self.assertEqual(shape, p1.shape())
self.assertEqual(shape, p2.shape())
self.assertEqual(values1, p1.value.to_list())
self.assertEqual(values2, p2.value.to_list())
self.assertTrue("a" in p1.stats)
self.assertTrue("b" in p2.stats)
self.assertEqual(stats1, p1.stats["a"].to_list())
self.assertEqual(stats2, p2.stats["b"].to_list())
def test_TensorTest_CheckCopyValidToNew(self):
for dev in TensorTest.devices:
print(dev)
tmp = tF.raw_input(Shape([2], 3), [1, 2, 3, 4, 5, 6], dev)
x = Tensor(tmp)
self.assertTrue(x.valid())
self.assertTrue(tmp.valid())
self.assertEqual(Shape([2], 3), x.shape())
self.assertEqual(Shape([2], 3), tmp.shape())
self.assertEqual([1, 2, 3, 4, 5, 6], x.to_list())
self.assertEqual([1, 2, 3, 4, 5, 6], tmp.to_list())
def test_TensorTest_CheckNewScalarWithData(self):
for dev in TensorTest.devices:
x = tF.raw_input([], [1], dev)
x_ndarray = [
np.array([1]),
]
self.assertTrue(x.valid())
self.assertIs(dev, x.device())
self.assertEqual(Shape(), x.shape())
self.assertEqual([1], x.to_list())
self.assertEqual(1.0, x.to_float())
self.assertEqual(x_ndarray, x.to_ndarrays())
def test_TensorTest_CheckNewMatrixWithData(self):
for dev in TensorTest.devices:
data = [1, 2, 3, 4, 5, 6]
data_ndarray = [
np.array([[1, 3, 5], [2, 4, 6]]),
]
x = tF.raw_input([2, 3], data, dev)
self.assertTrue(x.valid())
self.assertIs(dev, x.device())
self.assertEqual(Shape([2, 3]), x.shape())
self.assertEqual(data, x.to_list())
with self.assertRaises(RuntimeError):
x.to_float()
self.assertTrue(np.array_equal(data_ndarray, x.to_ndarrays()))
def test_optimizer_add(self):
model = TestModel()
p = Parameter([5], I.Constant(0))
p.gradient = tF.raw_input([5], [1, 2, 3, 4, 5])
optimizer = O.Adam()
optimizer.set_weight_decay(1e-6)
optimizer.set_gradient_clipping(5)
optimizer.add(model)
optimizer.add(p)
self.assertEqual(p.gradient.to_list(), [1, 2, 3, 4, 5])
self.assertEqual(model.param.gradient.to_list(), [1, 2, 3, 4, 5])
optimizer.reset_gradients()
self.assertEqual(p.gradient.to_list(), [0, 0, 0, 0, 0])
self.assertEqual(model.param.gradient.to_list(), [0, 0, 0, 0, 0])
def test_device_instance(self):
tensor = tF.raw_input([], [0])
dev = tensor.device()
self.assertIs(dev, self.device)
node = F.raw_input([], [0])
dev = node.device()
self.assertIs(dev, self.device)
my_device = Naive()
self.assertIsNot(my_device, self.device)
node = F.raw_input([], [0], dev=my_device)
dev = node.device()
self.assertIs(dev, my_device)
param = Parameter([], I.Constant(1))
dev = param.device()
self.assertIs(dev, self.device)
def test_TensorTest_CheckNewMatrixMinibatchWithData(self):
for dev in TensorTest.devices:
data = [
3,
1,
4,
1,
5,
9,
2,
6,
5,
3,
5,
8,
9,
7,
9,
3,
2,
3,
8,
4,
6,
2,
6,
4,
]
data_ndarray = [
np.array([[3, 4, 5], [1, 1, 9]]),
np.array([[2, 5, 5], [6, 3, 8]]),
np.array([[9, 9, 2], [7, 3, 3]]),
np.array([[8, 6, 6], [4, 2, 4]]),
]
x = tF.raw_input(Shape([2, 3], 4), data, dev)
self.assertTrue(x.valid())
self.assertIs(dev, x.device())
self.assertEqual(Shape([2, 3], 4), x.shape())
self.assertEqual(data, x.to_list())
with self.assertRaises(RuntimeError):
x.to_float()
self.assertTrue(np.array_equal(data_ndarray, x.to_ndarrays()))
def __init__(self):
self.param = Parameter([5], I.Constant(0))
self.param.gradient = tF.raw_input([5], [1, 2, 3, 4, 5])
self.scan_attributes()
