做风格迁移学习时,在 target_feature=model(style_img).clone()发生错误，expected Tensor as element 0 in argument 0, but got tuple
然而style_img的格式是torch.cuda.FloatTensor
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
from PIL import Image
import matplotlib.pyplot as plt
import torchvision.transforms as transforms
import torchvision.models as models
import copy
use_cuda=torch.cuda.is_available()
dtype=torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
style='images/timg.jpg'
content='images/123.jpg'
style_weight=1000
content_weight=1
imsize=128
loader=transforms.Compose([
transforms.Resize(imsize),
transforms.ToTensor()
])
def image_loader(image_name):
image=Image.open(image_name)
image=Variable(loader(image))
image=image.unsqueeze(0)
return image
style_img=image_loader(style).type(dtype)
content_img=image_loader(content).type(dtype)
print(style_img)
assert style_img.size()==content_img.size()
def imshow(tensor,title=None):
image=tensor.clone().cpu()
image=image.view(3,imsize,imsize)
image=unloader(image)
plt.imshow(image)
if title is not None:
plt.title(title)
plt.pause(0.001)
unloader=transforms.ToPILImage()
plt.ion()
#
plt.figure()
imshow(style_img.data,title='Style Image')
#
plt.figure()
imshow(content_img.data,title='Content Image')
加载网络
cnn=models.vgg19(pretrained=True).features
if use_cuda:
cnn=cnn.cuda()
class ContentLoss(nn.Module):
def init(self,target,weight):
super(ContentLoss,self).__init__()
self.target=target.detach()*weight
self.weight=weight
self.criterion=nn.MSELoss()
def forward(self, *input):
self.loss=self.criterion(input*self.weight,self.target)
self.output=input
return self.output
def backward(self,retain_graph=True):
self.loss.backward(retain_graph=retain_graph)
return self.loss
class StyleLoss(nn.Module):
def init(self,target,weight):
super(StyleLoss,self).__init__()
self.target=target.detach()*weight
self.weight=weight
#self.gram=GramMatrix()
self.criterion=nn.MSELoss()
def forward(self,input):
self.output=input.clone()
input=input.cuda()
self_G=Gram(input)
self_G.mul(self.weight)
self.loss=self.criterion(self_G,self.target)
return self.output
def backward(self,retain_graph=True):
self.loss.backward(retain_graph=retain_graph)
return self.loss
def Gram(input):
a,b,c,d=input.size()
features=input.view(a*b,c*d)
G=torch.mm(features,features.t())
return G.div(a*b*c*d)
content_layers=['conv_4']
style_layers=['conv_1','conv_2','conv_3','conv_4','conv_5']
content_losses=[]
style_losses=[]
model=nn.Sequential()
if use_cuda:
model=model.cuda()
i=1
for layer in list(cnn):
if isinstance(layer,nn.Conv2d):
name='conv_'+str(i)
model.add_module(name,layer)
if name in content_layers:
target=model(content_img).clone()
content_loss=ContentLoss(target,content_weight)
content_loss=content_loss.cuda() if use_cuda else content_loss
model.add_module('content_loss'+str(i),content_loss)
content_losses.append(content_loss)
if name in style_layers:
target_feature=model(style_img).clone()
target_feature=target_feature.cuda() if use_cuda else target_feature
target_feature_gram=Gram(target_feature)
style_loss=StyleLoss(target_feature_gram,style_weight)
style_loss=style_loss.cuda() if use_cuda else style_loss
model.add_module("style_loss"+str(i),style_loss)
style_losses.append(style_loss)
if isinstance(layer,nn.ReLU):
name='relu'+str(i)
model.add_module(name,layer)
i+=1
if isinstance(layer,nn.MaxPool2d):
name="pool"+str(i)
model.add_module(name,layer)
input_img=Variable(torch.randn(content_img.data.size()))
if use_cuda:
input_img=input_img.cuda()
content_img=content_img.cuda()
style_img=style_img.cuda()
plt.figure()
imshow(input_img.data,title='Input Image')
