def __init__(self,
image=None,
layer_ids=None,
layer_weights=None,
lambda_loss=1e-2,
k=3,
s=1,
semantic_style_image=None,
semantic_content_image=None,
gamma=None,
gamma_scale=0.4):
'''Create PatchStyle.
Kwargs
------
image : image
Style image to be matched.
layer_ids : list
Optional list of raw network layers to compute feature losses at.
If None defaults to [6,8,10].
layer_weights : list
Optional weights for each layer loss. If None uniform weights are
applied.
lambda_loss : scalar
Lagrange multiplier strengthness for this loss vs. other losses.
k : number
Kernel size
s : number
Stride
semantic_style_image : image
Semantic map of style
semantic_content_image : image
Semantic map of content
gamma : list, scalar
Optional gamma factor for channel concatenation per layer. If None,
auto-tunes gamma per layer.
gamma_scale : scale
Weights the auto-tuned gamma factor.
'''
super(SemanticStyle, self).__init__(image=image,
layer_ids=layer_ids,
layer_weights=layer_weights,
lambda_loss=lambda_loss,
k=k,
s=s)
self.semantic_style_image = to_np(semantic_style_image)
self.semantic_content_image = to_np(semantic_content_image)
self.gamma = gamma
self.gamma_scale = gamma_scale
def __init__(self,
image=None,
layer_ids=None,
layer_weights=None,
lambda_loss=1e4):
'''Create GramStyle.
Kwargs
------
image : image
Style image to be matched.
layer_ids : list
Optional list of raw network layers to compute feature losses at.
If None defaults to [6,8,10].
layer_weights : list
Optional weights for each layer loss. If None uniform weights are
applied.
lambda_loss : scalar
Lagrange multiplier strengthness for this loss vs. other losses.
'''
if layer_ids is None:
layer_ids = [6, 8, 10]
super(GramStyle, self).__init__(layer_ids, lambda_loss)
self.layer_weights = layer_weights
self.image = to_np(image)
def __init__(self, image=None, layer_id=8, lambda_loss=1e-3):
'''Create Content loss provider.
Kwargs
------
image : image
Style image to be matched.
layer_id : number
Layer index to compute feature losses at.
lambda_loss : scalar
Lagrange multiplier strengthness for this loss vs. other losses.
'''
super(Content, self).__init__([layer_id], lambda_loss)
if image is not None:
self.image = to_np(image)
else:
self.image = None