def test_ImagePyramid_iter_resize(self):
class TestOperator(PixelComparisonOperator):
def target_image_to_repr(self, image):
return image, None
def input_image_to_repr(self, image, ctx):
pass
def calculate_score(self, input_repr, target_repr, ctx):
pass
initial_image_size = (5, 4)
edge_sizes = (2, 4)
torch.manual_seed(0)
target_guide = torch.rand((1, 3, *initial_image_size))
target_image = torch.rand((1, 3, *initial_image_size))
input_guide = torch.rand((1, 3, *initial_image_size))
aspect_ratio = calculate_aspect_ratio(initial_image_size)
image_sizes = [
edge_to_image_size(edge_size, aspect_ratio) for edge_size in edge_sizes
]
op = TestOperator()
op.set_target_guide(target_guide)
op.set_target_image(target_image)
op.set_input_guide(input_guide)
image_pyramid = pyramid.ImagePyramid(edge_sizes, 1, resize_targets=(op,))
for pyramid_level, image_size in zip(image_pyramid, image_sizes):
for attr in ("target_guide", "target_image", "input_guide"):
with self.subTest(attr, pyramid_level=pyramid_level):
actual = extract_image_size(getattr(op, attr))
desired = image_size
self.assertTupleEqual(actual, desired)
def image_pyramid(
hyper_parameters: Optional[HyperParameters] = None,
**image_pyramid_kwargs: Any,
) -> pyramid.ImagePyramid:
r"""Image pyramid from :cite:`GEB+2017`.
Args:
hyper_parameters: If omitted,
:func:`~pystiche_papers.gatys_et_al_2017.hyper_parameters` is used.
**image_pyramid_kwargs: Additional parameters of a
:class:`pystiche.pyramid.ImagePyramid`.
.. seealso::
- :class:`pystiche.pyramid.ImagePyramid`
"""
if hyper_parameters is None:
hyper_parameters = _hyper_parameters()
return pyramid.ImagePyramid(
hyper_parameters.image_pyramid.edge_sizes,
hyper_parameters.image_pyramid.num_steps,
**image_pyramid_kwargs,
)
#
# By default the ``edge_sizes`` correspond to the shorter ``edge`` of the images. To
# change that you can pass ``edge="long"``. For fine-grained control you can also
# pass a sequence comprising ``"short"`` and ``"long"`` to select the ``edge`` for
# each level separately. Its length has to match the length of ``edge_sizes``.
#
# .. note::
#
# For a fine-grained control over the number of steps on each level you can pass a
# sequence to select the ``num_steps`` for each level separately. Its length has to
# match the length of ``edge_sizes``.
edge_sizes = (250, 500)
num_steps = 200
image_pyramid = pyramid.ImagePyramid(edge_sizes,
num_steps,
resize_targets=(criterion, ))
print(image_pyramid)
########################################################################################
# With a pyramid the NST is performed by
# :func:`~pystiche.optim.pyramid_image_optimization`. We time the execution and show
# the result afterwards.
#
# .. note::
#
# We regenerate the ``input_image`` since it was changed inplace during the first
# optimization.
input_image = get_input_image(starting_point, content_image=content_image)
#
# By default the ``edge_sizes`` correspond to the shorter ``edge`` of the images. To
# change that you can pass ``edge="long"``. For fine-grained control you can also
# pass a sequence comprising ``"short"`` and ``"long"`` to select the ``edge`` for
# each level separately. Its length has to match the length of ``edge_sizes``.
#
# .. note::
#
# For a fine-grained control over the number of steps on each level you can pass a
# sequence to select the ``num_steps`` for each level separately. Its length has to
# match the length of ``edge_sizes``.
edge_sizes = (250, 500)
num_steps = 200
image_pyramid = pyramid.ImagePyramid(edge_sizes,
num_steps,
resize_targets=(perceptual_loss, ))
print(image_pyramid)
########################################################################################
# With a pyramid the NST is performed by
# :func:`~pystiche.optim.pyramid_image_optimization`. We time the execution and show
# the result afterwards.
#
# .. note::
#
# We regenerate the ``input_image`` since it was changed inplace during the first
# optimization.
input_image = get_input_image(starting_point, content_image=content_image)