def test_backward_brackets():
"""Tests that the backwards operation through the logsignature gives the correct values."""
for class_ in (False, True):
for device in h.get_devices():
for batch_size, input_stream, input_channels, basepoint in h.random_sizes_and_basepoint():
for depth in (1, 2, 4, 6):
for mode in (h.brackets_mode,):
stream = random.choice([False, True])
inverse = random.choice([False, True])
_test_backward(class_, device, batch_size, input_stream, input_channels, depth, stream,
basepoint, inverse, mode)
def test_backward():
"""Tests that the backwards operation through the signature gives the correct values."""
for class_ in (False, True):
for device in h.get_devices():
for batch_size, input_stream, input_channels, basepoint in h.random_sizes_and_basepoint():
for depth in (1, 2, 4, 6):
for stream in (False, True):
for inverse in (False, True):
for initial in (None, h.without_grad, h.with_grad):
for scalar_term in (False, True):
_test_backward(class_, device, batch_size, input_stream, input_channels, depth,
stream, basepoint, inverse, initial, scalar_term)
def test_no_adjustments():
"""Tests that the logsignature computations don't modify any memory that they're not supposed to."""
for class_ in (False, True):
for device in h.get_devices():
for batch_size, input_stream, input_channels, basepoint in h.random_sizes_and_basepoint():
for depth in (1, 2, 5):
for mode in h.all_modes:
path_grad = random.choice([False, True])
stream = random.choice([False, True])
inverse = random.choice([False, True])
_test_no_adjustments(class_, device, batch_size, input_stream, input_channels, depth, stream,
basepoint, inverse, mode, path_grad)
def test_no_adjustments():
"""Tests that the signature computations don't modify any memory that they're not supposed to."""
for class_ in (False, True):
for path_grad in (False, True):
for device in h.get_devices():
for batch_size, input_stream, input_channels, basepoint in h.random_sizes_and_basepoint():
for depth in (1, 2, 5):
for stream in (False, True):
for inverse in (False, True):
for initial in (None, h.without_grad, h.with_grad):
for scalar_term in (False, True):
_test_no_adjustments(class_, device, batch_size, input_stream, input_channels,
depth, stream, basepoint, inverse, initial, path_grad,
scalar_term)
def test_repeat_and_memory_leaks():
"""Performs two separate tests.
First, that the computations are deterministic, and always give the same result when run multiple times; in
particular that using the class signatory.LogSignature multiple times is fine.
Second, that there are no memory leaks.
"""
for class_ in (False, True):
for path_grad in (False, True):
for batch_size, input_stream, input_channels, basepoint in h.random_sizes_and_basepoint():
for depth in (1, 2, 5):
for mode in h.all_modes:
stream = random.choice([False, True])
inverse = random.choice([False, True])
_test_repeat_and_memory_leaks(class_, path_grad, batch_size, input_stream, input_channels,
depth, stream, basepoint, inverse, mode)
def test_repeat_and_memory_leaks():
"""Performs two separate tests.
First, that the computations are deterministic, and always give the same result when run multiple times; in
particular that using the class signatory.Signature multiple times is fine.
Second, that there are no memory leaks.
"""
for class_ in (False, True):
for path_grad in (False, True):
for batch_size, input_stream, input_channels, basepoint in h.random_sizes_and_basepoint():
for depth in (1, 2, 5):
for stream in (False, True):
for inverse in (False, True):
for initial in (None, h.without_grad, h.with_grad):
for scalar_term in (False, True):
_test_repeat_and_memory_leaks(class_, path_grad, batch_size, input_stream,
input_channels, depth, stream, basepoint, inverse,
initial, scalar_term)
