def test_convolve_data_adjoint_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 dtypes:
with self.subTest(dtype=dtype, device=device):
output = xp.ones([1, 5], dtype=dtype)
filt = xp.ones([1, 3], dtype=dtype)
data_shape = [1, 3]
data = backend.to_device(
conv.convolve_data_adjoint(output,
filt,
data_shape,
mode=mode))
npt.assert_allclose(data, [[3, 3, 3]], atol=1e-5)
output = xp.ones([1, 4], dtype=dtype)
filt = xp.ones([1, 2], dtype=dtype)
data_shape = [1, 3]
data = backend.to_device(
conv.convolve_data_adjoint(output,
filt,
data_shape,
mode=mode))
npt.assert_allclose(data, [[2, 2, 2]], atol=1e-5)
output = xp.ones([2, 1, 5], dtype=dtype)
filt = xp.ones([2, 1, 1, 3], dtype=dtype)
data_shape = [1, 1, 3]
data = backend.to_device(
conv.convolve_data_adjoint(output,
filt,
data_shape,
mode=mode,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(data, [[[6, 6, 6]]], atol=1e-5)
output = xp.ones([2, 1, 5], dtype=dtype)
filt = xp.ones([2, 1, 1, 3], dtype=dtype)
data_shape = [1, 1, 8]
strides = [1, 2]
data = backend.to_device(
conv.convolve_data_adjoint(output,
filt,
data_shape,
mode=mode,
strides=strides,
multi_channel=True),
backend.cpu_device)
npt.assert_allclose(data, [[[4, 2, 4, 2, 4, 2, 4, 2]]],
atol=1e-5)
def _apply(self, input):
return conv.convolve_data_adjoint(input,
self.filt,
self.oshape,
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_data_adjoint(
input, filt, self.oshape,
mode=self.mode, strides=self.strides,
multi_channel=self.multi_channel)