def test_PerceptualLoss_set_style_image(self):
torch.manual_seed(0)
image = torch.rand(1, 1, 100, 100)
content_loss = FeatureReconstructionOperator(
SequentialEncoder((nn.Conv2d(1, 1, 1),))
)
style_loss = FeatureReconstructionOperator(
SequentialEncoder((nn.Conv2d(1, 1, 1),))
)
perceptual_loss = loss.PerceptualLoss(content_loss, style_loss)
perceptual_loss.set_style_image(image)
actual = style_loss.target_image
desired = image
self.assertTensorAlmostEqual(actual, desired)
print(f"I'm working with {device}")
images = demo_images()
images.download()
########################################################################################
# At first we define a :class:`~pystiche.loss.perceptual.PerceptualLoss` that is used
# as optimization ``criterion``.
multi_layer_encoder = vgg19_multi_layer_encoder()
content_layer = "relu4_2"
content_encoder = multi_layer_encoder.extract_single_layer_encoder(
content_layer)
content_weight = 1e0
content_loss = FeatureReconstructionOperator(content_encoder,
score_weight=content_weight)
style_layers = ("relu3_1", "relu4_1")
style_weight = 2e0
def get_style_op(encoder, layer_weight):
patch_size = 3
return MRFOperator(encoder,
patch_size,
stride=2,
score_weight=layer_weight)
style_loss = MultiLayerEncodingOperator(
multi_layer_encoder,
def get_guided_perceptual_loss():
content_loss = FeatureReconstructionOperator(
SequentialEncoder((nn.Conv2d(1, 1, 1),))
)
style_loss = MultiRegionOperator(regions, get_op)
return loss.GuidedPerceptualLoss(content_loss, style_loss)