def test_convolve_full(self):
mode = 'full'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
for device in devices:
xp = device.xp
with device:
for dtype in [np.float32, np.float64, np.complex64, np.complex128]:
x = util.dirac([1, 3], device=device, dtype=dtype)
W = xp.ones([1, 3], dtype=dtype)
y = backend.to_device(conv.convolve(x, W, mode=mode), backend.cpu_device)
npt.assert_allclose(y, [[0, 1, 1, 1, 0]], atol=1e-5)
x = util.dirac([1, 3], device=device, dtype=dtype)
W = xp.ones([1, 2], dtype=dtype)
y = backend.to_device(conv.convolve(x, W, mode=mode), backend.cpu_device)
npt.assert_allclose(y, [[0, 1, 1, 0]], atol=1e-5)
x = util.dirac([1, 3], device=device, dtype=dtype)
W = xp.ones([2, 1, 3], dtype=dtype)
y = backend.to_device(conv.convolve(x, W, mode=mode,
output_multi_channel=True), backend.cpu_device)
npt.assert_allclose(y, [[[0, 1, 1, 1, 0]],
[[0, 1, 1, 1, 0]]], atol=1e-5)
def test_convolve_valid(self):
mode = 'valid'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
for D in [1, 2, 3]:
for device in devices:
xp = device.xp
with device:
for dtype in dtypes:
with self.subTest(D=D, dtype=dtype, device=device):
data = util.dirac([3] + [1] * (D - 1),
device=device, dtype=dtype)
filt = xp.ones([3] + [1] * (D - 1), dtype=dtype)
output = backend.to_device(conv.convolve(
data, filt, mode=mode))
npt.assert_allclose(
output,
np.ones([1] * D), atol=1e-5)
data = util.dirac([3] + [1] * (D - 1),
device=device, dtype=dtype)
filt = xp.ones([2] + [1] * (D - 1), dtype=dtype)
output = backend.to_device(conv.convolve(
data, filt, mode=mode))
npt.assert_allclose(
output,
np.ones([2] + [1] * (D - 1)), atol=1e-5)
data = util.dirac([1, 3] + [1] * (D - 1),
device=device,
dtype=dtype)
filt = xp.ones([2, 1, 3] + [1] * (D - 1),
dtype=dtype)
output = backend.to_device(
conv.convolve(data, filt,
mode=mode,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(
output,
np.ones([2, 1] + [1] * (D - 1)),
atol=1e-5)
data = util.dirac([1, 3] + [1] * (D - 1),
device=device,
dtype=dtype)
filt = xp.ones([2, 1, 3] + [1] * (D - 1),
dtype=dtype)
strides = [2] + [1] * (D - 1)
output = backend.to_device(
conv.convolve(data, filt,
mode=mode, strides=strides,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(
output,
np.ones([2, 1] + [1] * (D - 1)),
atol=1e-5)
def test_convolve_full(self):
mode = 'full'
devices = [backend.cpu_device]
if config.cupy_enabled:
devices.append(backend.Device(0))
dtypes = [np.float32, np.float64, np.complex64, np.complex128]
for device in devices:
xp = device.xp
with device:
for dtype in dtypes:
with self.subTest(dtype=dtype, device=device):
data = util.dirac([1, 3], device=device, dtype=dtype)
filt = xp.ones([1, 3], dtype=dtype)
output = backend.to_device(
conv.convolve(data, filt, mode=mode))
npt.assert_allclose(output, [[0, 1, 1, 1, 0]],
atol=1e-5)
data = util.dirac([1, 3], device=device, dtype=dtype)
filt = xp.ones([1, 2], dtype=dtype)
output = backend.to_device(
conv.convolve(data, filt, mode=mode))
npt.assert_allclose(output, [[0, 1, 1, 0]], atol=1e-5)
data = util.dirac([1, 1, 3],
device=device,
dtype=dtype)
filt = xp.ones([2, 1, 1, 3], dtype=dtype)
output = backend.to_device(
conv.convolve(data,
filt,
mode=mode,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(
output, [[[0, 1, 1, 1, 0]], [[0, 1, 1, 1, 0]]],
atol=1e-5)
data = util.dirac([1, 1, 3],
device=device,
dtype=dtype)
filt = xp.ones([2, 1, 1, 3], dtype=dtype)
strides = [1, 2]
output = backend.to_device(
conv.convolve(data,
filt,
mode=mode,
strides=strides,
multi_channel=True))
npt.assert_allclose(output, [[[0, 1, 0]], [[0, 1, 0]]],
atol=1e-5)
def _apply(self, input):
data = backend.to_device(self.data, backend.get_device(input))
return conv.convolve(data,
input,
mode=self.mode,
strides=self.strides,
multi_channel=self.multi_channel)
def _apply(self, input):
device = backend.get_device(input)
filt = backend.to_device(self.filt, device)
with device:
return conv.convolve(input, filt, mode=self.mode,
strides=self.strides,
multi_channel=self.multi_channel)
def _apply(self, input):
device = backend.get_device(input)
x = backend.to_device(self.x, backend.get_device(input))
with device:
x = x.astype(input.dtype, copy=False)
return conv.convolve(x,
input,
mode=self.mode,
input_multi_channel=self.input_multi_channel,
output_multi_channel=self.output_multi_channel)
def _apply(self, input):
return conv.convolve(input,
self.filt,
mode=self.mode,
strides=self.strides,
multi_channel=self.multi_channel)
def _apply(self, input):
return conv.convolve(self.x,
input,
mode=self.mode,
input_multi_channel=self.input_multi_channel,
output_multi_channel=self.output_multi_channel)
