def __fit_one(self, link, content4_2, style3_2,style4_2):
xp = self.xp
link.zerograds()
layer3_2,layer4_2 = self.model(link.x)
if self.keep_color:
#trans_layers = self.model(util.gray(link.x))
print "don't keep color!"
loss_info = []
loss = Variable(xp.zeros((), dtype=np.float32))
#layer = layers[name]
content_loss = self.content_weight * F.mean_squared_error(layer4_2, Variable(content4_2))
loss_info.append(('content_', float(content_loss.data)))
loss += content_loss
style_patch, style_patch_norm = style3_2
near,size,size2 = util.nearest_neighbor_patch(layer3_2, style_patch, style_patch_norm)
style_loss = self.style_weight * (F.sum(F.square(layer3_2))*size2/size-2*F.sum(near)/size)
loss_info.append(('style_', float(style_loss.data)))
loss+=style_loss
style_patch, style_patch_norm = style4_2
near,size,size2 = util.nearest_neighbor_patch(layer4_2, style_patch, style_patch_norm)
style_loss = self.style_weight *1.5* (F.sum(F.square(layer4_2))*size2/size-2*F.sum(near)/size)
loss_info.append(('style_', float(style_loss.data)))
loss+= style_loss
tv_loss = self.tv_weight * util.total_variation(link.x)
loss_info.append(('tv', float(tv_loss.data)))
loss+=tv_loss
loss.backward()
self.optimizer.update()
return loss_info
def __fit_one(self, link, content_layers, style_grams):
xp = self.xp
link.zerograds()
layers = self.model(link.x)
if self.keep_color:
trans_layers = self.model(util.gray(link.x))
else:
trans_layers = layers
loss_info = []
loss = Variable(xp.zeros((), dtype=np.float32))
for name, content_layer in content_layers:
layer = layers[name]
content_loss = self.content_weight * F.mean_squared_error(layer, Variable(content_layer.data))
loss_info.append(('content_' + name, float(content_loss.data)))
loss += content_loss
for name, style_gram in style_grams:
gram = util.gram_matrix(trans_layers[name])
style_loss = self.style_weight * F.mean_squared_error(gram, Variable(style_gram.data))
loss_info.append(('style_' + name, float(style_loss.data)))
loss += style_loss
tv_loss = self.tv_weight * util.total_variation(link.x)
loss_info.append(('tv', float(tv_loss.data)))
loss += tv_loss
loss.backward()
self.optimizer.update()
return loss_info
def __fit_one(self, link, content_layers, style_patches):
xp = self.xp
link.zerograds()
layers = self.model(link.x)
if self.keep_color:
trans_layers = self.model(util.gray(link.x))
else:
trans_layers = layers
loss_info = []
loss = Variable(xp.zeros((), dtype=np.float32))
for name, content_layer in content_layers:
layer = layers[name]
content_loss = self.content_weight * F.mean_squared_error(
layer, Variable(content_layer.data))
loss_info.append(('content_' + name, float(content_loss.data)))
loss += content_loss
for name, style_patch, style_patch_norm in style_patches:
patch = trans_layers[name]
near, size, size2 = util.nearest_neighbor_patch(
patch, style_patch, style_patch_norm)
style_loss = self.style_weight * (
F.sum(F.square(patch)) * size2 / size - 2 * F.sum(near) / size)
loss_info.append(('style_' + name, float(style_loss.data)))
loss += style_loss
tv_loss = self.tv_weight * util.total_variation(link.x)
loss_info.append(('tv', float(tv_loss.data)))
loss += tv_loss
loss.backward()
self.optimizer.update()
return loss_info
