def test_hyper_parameters_style_loss(subtests, impl_params):
hyper_parameters = paper.hyper_parameters(impl_params=impl_params)
sub_params = "style_loss"
assert sub_params in hyper_parameters
hyper_parameters = getattr(hyper_parameters, sub_params)
if impl_params:
layers, num_channels = zip(
("relu1_1", 64),
("relu2_1", 128),
("relu3_1", 256),
("relu4_1", 512),
("relu5_1", 512),
)
layer_weights = pytest.approx([1 / n**2 for n in num_channels])
else:
layers = ("conv1_1", "conv2_1", "conv3_1", "conv4_1", "conv5_1")
layer_weights = "mean"
with subtests.test("layer"):
assert hyper_parameters.layers == layers
with subtests.test("layer_weights"):
assert hyper_parameters.layer_weights == layer_weights
with subtests.test("score_weight"):
assert hyper_parameters.score_weight == pytest.approx(1e3)
def test_nst_smoke(subtests, mocker, content_image, style_image):
mock = mocker.patch("pystiche.optim.image_optimization")
paper.nst(content_image, style_image)
args, kwargs = mock.call_args
input_image, criterion = args
optimizer = kwargs["optimizer"]
num_steps = kwargs["num_steps"]
preprocessor = kwargs["preprocessor"]
postprocessor = kwargs["postprocessor"]
hyper_parameters = paper.hyper_parameters().nst
with subtests.test("input_image"):
ptu.assert_allclose(input_image, content_image)
with subtests.test("criterion"):
assert isinstance(criterion, type(paper.perceptual_loss()))
with subtests.test("optimizer"):
assert optimizer is paper.optimizer
with subtests.test("num_steps"):
assert num_steps == hyper_parameters.num_steps
with subtests.test("preprocessor"):
assert isinstance(preprocessor, type(paper.preprocessor()))
with subtests.test("postprocessor"):
assert isinstance(postprocessor, type(paper.postprocessor()))
def test_content_loss(subtests):
content_loss = paper.content_loss()
assert isinstance(content_loss, paper.FeatureReconstructionLoss)
hyper_parameters = paper.hyper_parameters().content_loss
with subtests.test("layer"):
assert content_loss.encoder.layer == hyper_parameters.layer
with subtests.test("score_weight"):
assert content_loss.score_weight == pytest.approx(
hyper_parameters.score_weight)
def test_hyper_parameters_content_loss(subtests, impl_params):
hyper_parameters = paper.hyper_parameters(impl_params=impl_params)
sub_params = "content_loss"
assert sub_params in hyper_parameters
hyper_parameters = getattr(hyper_parameters, sub_params)
with subtests.test("layer"):
assert hyper_parameters.layer == "relu4_2" if impl_params else "conv4_2"
with subtests.test("score_weight"):
assert hyper_parameters.score_weight == pytest.approx(1e0)
def figure_3(args):
images = paper.images()
images.download(args.image_source_dir)
hyper_parameters = paper.hyper_parameters(args.impl_params)
content_image = images["neckarfront"].read(
size=hyper_parameters.nst.image_size, device=args.device)
style_image = images["composition_vii"].read(
size=hyper_parameters.nst.image_size, device=args.device)
style_layers = hyper_parameters.style_loss.layers
layer_configs = [
style_layers[:idx + 1] for idx in range(len(style_layers))
]
score_weights = (1e5, 1e4, 1e3, 1e2)
params = "implementation" if args.impl_params else "paper"
for layers, score_weight in itertools.product(layer_configs,
score_weights):
row_label = layers[-1]
column_label = f"{1.0 / score_weight:.0e}"
print(
f"Replicating Figure 3 image in row {row_label} and column {column_label} "
f"with {params} parameters.")
hyper_parameters.style_loss.layers = layers
if args.impl_params:
hyper_parameters.style_loss.layer_weights = paper.compute_layer_weights(
layers)
hyper_parameters.style_loss.score_weight = score_weight
output_image = paper.nst(
content_image,
style_image,
impl_params=args.impl_params,
hyper_parameters=hyper_parameters,
)
filename = utils.make_output_filename(
"gatys_ecker_betghe_2016",
"fig_3",
row_label,
column_label,
impl_params=args.impl_params,
)
save_result(
output_image,
path.join(args.image_results_dir, filename),
)
def test_hyper_parameters_nst(subtests, impl_params):
hyper_parameters = paper.hyper_parameters(impl_params=impl_params)
sub_params = "nst"
assert sub_params in hyper_parameters
hyper_parameters = getattr(hyper_parameters, sub_params)
with subtests.test("num_steps"):
assert hyper_parameters.num_steps == 500
with subtests.test("starting_point"):
assert hyper_parameters.starting_point == "content" if impl_params else "random"
with subtests.test("image_size"):
assert hyper_parameters.image_size == 512
def figure_2(args):
images = paper.images()
images.download(args.image_source_dir)
hyper_parameters = paper.hyper_parameters()
content_image = images["neckarfront"].read(
size=hyper_parameters.nst.image_size, device=args.device)
class StyleImage:
def __init__(self, label, image, score_weight):
self.label = label
self.image = image.read(size=hyper_parameters.nst.image_size,
device=args.device)
self.score_weight = score_weight
style_images = (
StyleImage("B", images["shipwreck"], 1e3),
StyleImage("C", images["starry_night"], 1e3),
StyleImage("D", images["the_scream"], 1e3),
StyleImage("E", images["femme_nue_assise"], 1e4),
StyleImage("F", images["composition_vii"], 1e4),
)
for style_image in style_images:
print(f"Replicating Figure 2 {style_image.label}")
hyper_parameters.style_loss.score_weight = style_image.score_weight
output_image = paper.nst(
content_image,
style_image.image,
impl_params=args.impl_params,
hyper_parameters=hyper_parameters,
)
filename = utils.make_output_filename(
"gatys_ecker_betghe_2016",
"fig_2",
style_image.label,
impl_params=args.impl_params,
)
save_result(
output_image,
path.join(args.image_results_dir, filename),
)
def test_style_loss(subtests):
style_loss = paper.style_loss()
assert isinstance(style_loss, pystiche.loss.MultiLayerEncodingLoss)
hyper_parameters = paper.hyper_parameters().style_loss
with subtests.test("encoding_losses"):
assert all(
isinstance(loss, pystiche.loss.GramLoss)
for loss in style_loss.children())
layers, layer_weights = zip(*[(loss.encoder.layer, loss.score_weight)
for loss in style_loss.children()])
with subtests.test("layers"):
assert layers == hyper_parameters.layers
with subtests.test("layer_weights"):
assert layer_weights == pytest.approx(hyper_parameters.layer_weights)
with subtests.test("score_weight"):
assert style_loss.score_weight == pytest.approx(
hyper_parameters.score_weight)
def test_hyper_parameters(impl_params):
hyper_parameters = paper.hyper_parameters(impl_params=impl_params)
assert isinstance(hyper_parameters, HyperParameters)
