#--------------------------导包
import torch
from torch.autograd import Variable
import torchvision
from torchvision import transforms, models
import copy
from PIL import Image
import matplotlib.pyplot as plt
 
#--------------------------1.数据预处理，加载数据
transform = transforms.Compose([transforms.Resize([224,224]),
                               transforms.ToTensor()])
 
def loadimg(path = None):
    img = Image.open(path)
    img = transform(img)
    img = img.unsqueeze(0)
    return img
 
content_img = loadimg('images/1.jpg')  #入参是自己存放图片的位置
content_img = Variable(content_img).cuda()
style_img = loadimg('images/2.jpg')
style_img = Variable(style_img).cuda()
 
#--------------------------2.定义内容损失和风格损失
class Content_loss(torch.nn.Module):
    def __init__(self,weight,target):
        super(Content_loss,self).__init__()
        self.weight = weight
        self.target = target.detach()*weight
        self.loss_fn = torch.nn.MSELoss()
        
    def forward(self,in_put):
        self.loss = self.loss_fn(in_put*self.weight,self.target)
        return in_put
    
    def backward(self):
        self.loss.backward(retain_graph = True)
        return self.loss
 
class Gram_matrix(torch.nn.Module):
    def forward(self,in_put):
        a,b,c,d = in_put.size()
        feature = in_put.view(a*b,c*d)
        gram = torch.mm(feature,feature.t())
        return gram.div(a*b*c*d)
    
class Style_loss(torch.nn.Module):
    def __init__(self,weight,target):
        super(Style_loss,self).__init__()
        self.weight = weight
        self.target = target.detach()*weight
        self.loss_fn = torch.nn.MSELoss()
        self.gram = Gram_matrix()
        
    def forward(self,in_put):
        self.Gram = self.gram(in_put.clone())
        self.Gram.mul_(self.weight)
        self.loss = self.loss_fn(self.Gram,self.target)
        return in_put
    
    def backward(self):
        self.loss.backward(retain_graph = True)
        return self.loss
 
#--------------------------3.模型搭建
cnn = models.vgg16(pretrained = True).features  #迁移VGG16架构的特征提取部分
#指定整个卷积过程中分别在哪一层提取内容和风格
content_layer = ["Conv_3"]
style_layer = ["Conv_1","Conv_2","Conv_3","Conv_4"]
#定义保存内容损失和风格损失的列表
content_losses = []
style_losses = []
#指定内容损失和风格损失对最后得到的融合图片的影响权重
content_weight = 1
style_weight = 1000
 
#搭建图像风格迁移模型的代码如下：
new_model = torch.nn.Sequential() #建立空的模型
model = copy.deepcopy(cnn)
#deepcopy深复制，将被复制对象完全再复制一遍作为独立的新个体单独存在，改变原有被复制对象不会对已经复制出来的新对象产生影响。
#copy浅复制，并不会产生一个独立的对象单独存在，他只是将原有的数据块打上一个新标签
#所以当其中一个标签被改变的时候，数据块就会发生变化，另一个标签也会随之改变。
gram = Gram_matrix()
 
use_gpu = torch.cuda.is_available()
if use_gpu:
    model = model.cuda()
    new_model = new_model.cuda()
    gram = gram.cuda()
    
index = 1
#只使用迁移模型特征提取部分的前8层
for layer in list(model)[:8]:
    if isinstance(layer,torch.nn.Conv2d):
        name = "Conv_" + str(index)
        #使用add_module方法向空的模型加入指定的层次模块
        new_model.add_module(name,layer)
        if name in content_layer:
            target = new_model(content_img).clone()
            content_loss = Content_loss(content_weight,target)
            new_model.add_module("content_loss_"+str(index),content_loss)
            content_losses.append(content_loss)
            
        if name in style_layer:
            target = new_model(style_img).clone()
            target = gram(target)
            style_loss = Style_loss(style_weight,target)
            new_model.add_module("style_loss_"+str(index),style_loss)
            style_losses.append(style_loss)
    
    if isinstance(layer,torch.nn.ReLU):
        name = "ReLU_"+str(index)
        new_model.add_module(name,layer)
        index = index + 1
        
    if isinstance(layer,torch.nn.MaxPool2d):
        name = "MaxPool_"+str(index)
        new_model.add_module(name,layer)
#构造优化器      
input_img = content_img.clone()
parameter = torch.nn.Parameter(input_img.data)
optimizer = torch.optim.LBFGS([parameter])
 
#--------------------------４.模型训练和参数优化
epoch_n = 300
epoch = [0]
while epoch[0] <= epoch_n:
 
    def closure():
        optimizer.zero_grad()
        style_score = 0
        content_score = 0
        parameter.data.clamp_(0,1)
        new_model(parameter)
        for sl in style_losses:
            style_score += sl.backward()
 
        for cl in content_losses:
            content_score += cl.backward()
 
        epoch[0] += 1
        if epoch[0] % 50 == 0:
            print('Epoch:{} Style_loss: {:4f} Content_loss: {:.4f}'.format(epoch[0], style_score.data.item(),
                                                                           content_score.data.item()))
        return style_score + content_score
 
    optimizer.step(closure)
    
#--------------------------5.对风格迁移图片输出
output = parameter.data
unloader = transforms.ToPILImage()  # 重新转化成PIL图像格式
 
plt.ion()
plt.figure()
def imshow(tensor, title=None):
    image = tensor.clone().cpu()  # 克隆tensor，改变时不影响被克隆的tensor
    image = image.view(3, 224, 224)  # 转换维度
    image = unloader(image)
    plt.imshow(image)
    if title is not None:
        plt.title(title)
    plt.pause(0.001) # 稍作停顿，以便更新图表
imshow(output, title='Output Image')
 
# 设置sphinx_gallery_thumbnail_number = 4
plt.ioff()
plt.show()