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#
# Copyright © 2020 Stephan Seitz <stephan.seitz@fau.de>
#
# Distributed under terms of the GPLv3 license.
"""
"""
assert pyronn_torch.cpp_extension
@pytest.mark.parametrize('with_texture', ('with_texture', False))
@pytest.mark.parametrize('with_backward', ('with_backward', False))
def test_projection(with_texture, with_backward):
projector = pyronn_torch.ConeBeamProjector(
(128, 128, 128),
(2.0, 2.0, 2.0),
(-127.5, -127.5, -127.5),
(2, 480, 620),
[1.0, 1.0],
(0, 0),
np.array([[[-3.10e+2, -1.20e+03, 0.00e+00, 1.86e+5],
[-2.40e+2, 0.00e+00, 1.20e+03, 1.44e+5],
[-1.00e+00, 0.00e+00, 0.00e+00, 6.00e+2]],
[[-2.89009888e+2, -1.20522754e+3, -1.02473585e-13,
1.86000000e+5],
[-2.39963440e+2, -4.18857765e+0, 1.20000000e+3,
1.44000000e+5],
[-9.99847710e-01, -1.74524058e-2, 0.00000000e+0,
6.00000000e+2]]])
)
volume = projector.new_volume_tensor(requires_grad=True if with_backward else False)
volume += 1.
result = projector.project_forward(volume, use_texture=with_texture)
assert result is not None
if with_backward:
assert volume.requires_grad
assert result.requires_grad
loss = result.mean()
loss.backward()
@pytest.mark.parametrize('with_texture', ('with_texture', False))
@pytest.mark.parametrize('with_backward', ('with_backward', False))
def test_projection_backward(with_texture, with_backward):
projector = pyronn_torch.ConeBeamProjector(
(128, 128, 128),
(2.0, 2.0, 2.0),
(-127.5, -127.5, -127.5),
(2, 480, 620),
[1.0, 1.0],
(0, 0),
np.array([[[-3.10e+2, -1.20e+03, 0.00e+00, 1.86e+5],
[-2.40e+2, 0.00e+00, 1.20e+03, 1.44e+5],
[-1.00e+00, 0.00e+00, 0.00e+00, 6.00e+2]],
[[-2.89009888e+2, -1.20522754e+3, -1.02473585e-13,
1.86000000e+5],
[-2.39963440e+2, -4.18857765e+0, 1.20000000e+3,
1.44000000e+5],
[-9.99847710e-01, -1.74524058e-2, 0.00000000e+0,
6.00000000e+2]]])
)
projection = projector.new_projection_tensor(requires_grad=True if with_backward else False)
projection += 1.
result = projector.project_backward(projection, use_texture=with_texture)
assert result is not None
if with_backward:
assert projection.requires_grad
assert result.requires_grad
loss = result.mean()
loss.backward()
@pytest.mark.parametrize('with_backward', ('with_backward', False))
def test_conrad_config(with_backward, with_texture=True):
import pytest
pytest.importorskip("pyconrad")
projector = pyronn_torch.ConeBeamProjector.from_conrad_config()
volume = projector.new_volume_tensor(requires_grad=True if with_backward else False)
result = projector.project_forward(volume, use_texture=with_texture)
import pyconrad.autoinit
pyconrad.imshow(result)
assert result is not None
if with_backward:
assert volume.requires_grad
assert result.requires_grad
loss = result.mean()
loss.backward()
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def test_projection_backward_conrad(with_texture=True, with_backward=True):
import pytest
pytest.importorskip("pyconrad")
projector = pyronn_torch.ConeBeamProjector.from_conrad_config()
projection = projector.new_projection_tensor(requires_grad=True if with_backward else False)
projection += 1000.
result = projector.project_backward(projection, use_texture=with_texture)
import pyconrad.autoinit
pyconrad.imshow(result)
assert result.shape == projector._volume_shape
assert result is not None
if with_backward:
assert projection.requires_grad
assert result.requires_grad
loss = result.mean()
loss.backward()
def test_conrad_forward_backward():
import pytest
pytest.importorskip("pyconrad")
projector = pyronn_torch.ConeBeamProjector.from_conrad_config()
volume = projector.new_volume_tensor()
volume += 1.
result = projector.project_forward(volume)
reco = projector.project_backward(result)
import pyconrad.autoinit
pyconrad.imshow(reco)
assert result is not None
assert reco is not None
while True:
pass