def test_utils_astensor1d(is_varnode):
if is_varnode:
network = Network()
else:
network = None
reference = make_tensor(0, network)
# literal
x = [1, 2, 3]
for dtype in [None, "float32"]:
xx = astensor1d(x, reference, dtype=dtype)
assert isinstance(xx, type(reference))
np.testing.assert_equal(xx.numpy(), x)
# numpy array
x = np.asarray([1, 2, 3], dtype="int32")
for dtype in [None, "float32"]:
xx = astensor1d(x, reference, dtype=dtype)
assert isinstance(xx, type(reference))
np.testing.assert_equal(xx.numpy(), x.astype(dtype) if dtype else x)
# tensor
x = make_tensor([1, 2, 3], network)
for dtype in [None, "float32"]:
xx = astensor1d(x, reference, dtype=dtype)
assert isinstance(xx, type(reference))
np.testing.assert_equal(xx.numpy(), x.numpy())
# mixed
x = [1, make_tensor(2, network), 3]
for dtype in [None, "float32"]:
xx = astensor1d(x, reference, dtype=dtype)
assert isinstance(xx, type(reference))
np.testing.assert_equal(xx.numpy(), [1, 2, 3])
def test_utils_astensor1d():
reference = tensor(0)
# literal
x = [1, 2, 3]
for dtype in [None, "float32"]:
xx = astensor1d(x, reference, dtype=dtype)
assert type(xx) is tensor
np.testing.assert_equal(xx.numpy(), x)
# numpy array
x = np.asarray([1, 2, 3], dtype="int32")
for dtype in [None, "float32"]:
xx = astensor1d(x, reference, dtype=dtype)
assert type(xx) is tensor
np.testing.assert_equal(xx.numpy(), x.astype(dtype) if dtype else x)
# tensor
x = tensor([1, 2, 3], dtype="int32")
for dtype in [None, "float32"]:
xx = astensor1d(x, reference, dtype=dtype)
assert type(xx) is tensor
np.testing.assert_equal(xx.numpy(), x.numpy())
# mixed
x = [1, tensor(2), 3]
for dtype in [None, "float32"]:
xx = astensor1d(x, reference, dtype=dtype)
assert type(xx) is tensor
np.testing.assert_equal(xx.numpy(), [1, 2, 3])
def forward(self, input):
A = input.shape[0]
shape = astensor1d((A, A),
self.data,
dtype="int32",
device=input.device)
x = F.reshape(self.data, shape)
o = input + x
return o
def test_reshape_shape_inference(is_varnode):
if is_varnode:
network = Network()
saved_symbolic_shape = set_symbolic_shape(False)
else:
network = None
x_shape_known = make_tensor([1, 2, 3, 4], network)
x_shape_unknown = F.broadcast_to(make_tensor([1.0], network),
shape=make_tensor([1, 1, 1, 1],
network).sum())
tshp_unknown = astensor1d(
(make_tensor([2], network), make_tensor([2], network)), x_shape_known)
tshp_known = astensor1d((2, 2), x_shape_known)
tshp_known_unspec = astensor1d((2, -1), x_shape_known)
def check_shape(output, target):
source = output.shape
if isinstance(source, tensor):
source = source.numpy()
np.testing.assert_equal(source, target.shape)
def func(x, target_shape):
return x.reshape(target_shape)
cases = [
{
"input": [x_shape_known, tshp_unknown],
"output": [
np.zeros((2, 2)),
]
},
{
"input": [x_shape_unknown, tshp_unknown],
"output": [
np.zeros((2, 2)),
]
},
{
"input": [x_shape_known, tshp_known],
"output": [
np.zeros((2, 2)),
]
},
{
"input": [x_shape_known, tshp_known_unspec],
"output": [
np.zeros((2, 2)),
]
},
{
"input": [x_shape_unknown, tshp_known],
"output": [
np.zeros((2, 2)),
]
},
{
"input": [x_shape_unknown, tshp_known_unspec],
"output": [
np.zeros((2, 2)),
]
},
]
opr_test(cases,
func,
compare_fn=check_shape,
test_trace=True,
network=network)
if is_varnode:
set_symbolic_shape(saved_symbolic_shape)
