diff --git a/advanced_source/neural_style_tutorial.py b/advanced_source/neural_style_tutorial.py index 88f5705e583..79e63989fd7 100644 --- a/advanced_source/neural_style_tutorial.py +++ b/advanced_source/neural_style_tutorial.py @@ -286,7 +286,7 @@ def __init__(self, target, weight): self.criterion = nn.MSELoss() def forward(self, input): - self.loss = self.criterion.forward(input * self.weight, self.target) + self.loss = self.criterion(input * self.weight, self.target) self.output = input return self.output @@ -357,9 +357,9 @@ def __init__(self, target, weight): def forward(self, input): self.output = input.clone() - self.G = self.gram.forward(input) + self.G = self.gram(input) self.G.mul_(self.weight) - self.loss = self.criterion.forward(self.G, self.target) + self.loss = self.criterion(self.G, self.target) return self.output def backward(self, retain_variables=True): @@ -430,15 +430,15 @@ def get_style_model_and_losses(cnn, style_img, content_img, if name in content_layers: # add content loss: - target = model.forward(content_img).clone() + target = model(content_img).clone() content_loss = ContentLoss(target, content_weight) model.add_module("content_loss_" + str(i), content_loss) content_losses.append(content_loss) if name in style_layers: # add style loss: - target_feature = model.forward(style_img).clone() - target_feature_gram = gram.forward(target_feature) + target_feature = model(style_img).clone() + target_feature_gram = gram(target_feature) style_loss = StyleLoss(target_feature_gram, style_weight) model.add_module("style_loss_" + str(i), style_loss) style_losses.append(style_loss) @@ -449,15 +449,15 @@ def get_style_model_and_losses(cnn, style_img, content_img, if name in content_layers: # add content loss: - target = model.forward(content_img).clone() + target = model(content_img).clone() content_loss = ContentLoss(target, content_weight) model.add_module("content_loss_" + str(i), content_loss) content_losses.append(content_loss) if name in style_layers: # add style loss: - target_feature = model.forward(style_img).clone() - target_feature_gram = gram.forward(target_feature) + target_feature = model(style_img).clone() + target_feature_gram = gram(target_feature) style_loss = StyleLoss(target_feature_gram, style_weight) model.add_module("style_loss_" + str(i), style_loss) style_losses.append(style_loss) @@ -564,7 +564,7 @@ def closure(): input_param.data.clamp_(0, 1) optimizer.zero_grad() - model.forward(input_param) + model(input_param) style_score = 0 content_score = 0