def test_sigmerger_check():
# 0-d signals
assert SigMerger.check([Signal(0), Signal(0)])
assert not SigMerger.check([Signal(0), Signal(1)])
# compatible along first axis
assert SigMerger.check(
[Signal(np.empty((1, 2))),
Signal(np.empty((2, 2)))])
# compatible along second axis
assert SigMerger.check(
[Signal(np.empty(
(2, 1))), Signal(np.empty((2, 2)))], axis=1)
assert not SigMerger.check(
[Signal(np.empty(
(2, 1))), Signal(np.empty((2, 2)))], axis=0)
# shape mismatch
assert not SigMerger.check(
[Signal(np.empty(
(2, ))), Signal(np.empty((2, 2)))])
# mixed dtype
assert not SigMerger.check(
[Signal(np.empty(2, dtype=int)),
Signal(np.empty(2, dtype=float))])
s1 = Signal(np.empty(5))
s2 = Signal(np.empty(5))
# mixed signal and view
assert not SigMerger.check([s1, s1[:3]])
# mixed bases
assert not SigMerger.check([s1[:2], s2[2:]])
# compatible views
assert SigMerger.check([s1[:2], s1[2:]])
# sparse signals not mergeable
assert not SigMerger.check([
Signal(SparseMatrix([[0, 0]], 1.0, (1, 1))),
Signal(SparseMatrix([[0, 0]], 1.0, (1, 1))),
])
# same signal cannot appear twice
sig = Signal(0)
assert not SigMerger.check([sig, sig])
def test_transforms():
check_init_args(Dense, ["shape", "init"])
# No check_repr because dense matrices are usually too big
assert repr(Dense((1, 2), init=[[1, 1]])) == "Dense(shape=(1, 2))"
check_init_args(
Convolution,
[
"n_filters",
"input_shape",
"kernel_size",
"strides",
"padding",
"channels_last",
"init",
],
)
check_repr(Convolution(n_filters=3, input_shape=(1, 2, 3)))
check_repr(
Convolution(n_filters=3, input_shape=(1, 2, 3), kernel_size=(3, 2)))
check_repr(
Convolution(n_filters=3, input_shape=(1, 2, 3), channels_last=False))
assert (repr(Convolution(
n_filters=3,
input_shape=(1, 2,
3))) == "Convolution(n_filters=3, input_shape=(1, 2, 3))")
assert (repr(
Convolution(n_filters=3, input_shape=(1, 2, 3), kernel_size=(3, 2))
) == "Convolution(n_filters=3, input_shape=(1, 2, 3), kernel_size=(3, 2))")
assert (
repr(
Convolution(n_filters=3,
input_shape=(1, 2, 3),
channels_last=False)) ==
"Convolution(n_filters=3, input_shape=(1, 2, 3), channels_last=False)")
check_init_args(Sparse, ["shape", "indices", "init"])
# No check_repr because sparse matrices are usually too big
assert repr(Sparse((1, 1), indices=[[1, 1],
[1, 1]])) == "Sparse(shape=(1, 1))"
assert (repr(Sparse((1, 1), indices=[[1, 1], [1, 1], [1, 1]],
init=2)) == "Sparse(shape=(1, 1))")
check_init_args(SparseMatrix, ["indices", "data", "shape"])
check_repr(
SparseMatrix(indices=[[1, 2], [3, 4]], data=[5, 6], shape=(7, 8)))
assert repr(SparseMatrix(((1, 2), (3, 4)), (5, 6), (7, 8))).replace(
", dtype=int64",
"") == ("SparseMatrix(indices=array([[1, 2],\n [3, 4]]), "
"data=array([5, 6]), shape=(7, 8))")
check_init_args(ChannelShape, ["shape", "channels_last"])
check_repr(ChannelShape(shape=(1, 2, 3), channels_last=True))
assert (repr(ChannelShape(
(1, 2, 3))) == "ChannelShape(shape=(1, 2, 3), channels_last=True)")
assert (repr(ChannelShape((1, 2, 3), channels_last=False)) ==
"ChannelShape(shape=(1, 2, 3), channels_last=False)")
# __str__ always has channels last
assert str(ChannelShape((1, 2, 3))) == "(1, 2, ch=3)"
assert str(ChannelShape((1, 2, 3), channels_last=False)) == "(ch=1, 2, 3)"
check_init_args(NoTransform, ["size_in"])
check_repr(NoTransform(size_in=1))
for dimensions in range(2):
assert repr(
NoTransform(dimensions)) == "NoTransform(size_in=%d)" % dimensions