# # 固定风格迁移
#
# # 导入模块
# from __future__ import print_function
#
# import torch
# import torch.nn as nn
# import torch.nn.functional as F
# 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
#
# device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#
#
# # 载入图像
# imsize = 512 if torch.cuda.is_available() else 128
#
# loader = transforms.Compose([
# transforms.Resize([imsize, imsize]),
# transforms.ToTensor()])
#
#
# def image_loader(image_name):
# image = Image.open(image_name)
# # fake batch dimension required to fit network's input dimensions
# image = loader(image).unsqueeze(0)
# return image.to(device, torch.float)
#
# content_img = image_loader('./style_img.jpeg')
# style_img = image_loader('./content_img.jpeg')
#
# print(style_img.shape)
# print(content_img.shape)
#
#
# assert style_img.size() == content_img.size(), \
# "风格图像和内容图像需要大小一致"
#
#
#
# unloader = transforms.ToPILImage()
#
# plt.ion()
#
# def imshow(tensor, title=None):
# image = tensor.cpu().clone()
# image = image.squeeze(0)
# image = unloader(image)
# plt.imshow(image)
# if title is not None:
# plt.title(title)
# plt.pause(0.001) # pause a bit so that plots are updated
#
#
# plt.figure()
# imshow(style_img, title='Style Image')
#
# plt.figure()
# imshow(content_img, title='Content Image')
#
#
#
# # 损失函数
# # 内容损失
# class ContentLoss(nn.Module):
#
# def __init__(self, target,):
# super(ContentLoss, self).__init__()
# self.target = target.detach()
#
# def forward(self, input):
# self.loss = F.mse_loss(input, self.target)
# return input
#
#
#
# # 风格损失
# def gram_matrix(input):
# a, b, c, d = input.size()
#
# features = input.view(a * b, c * d)
#
# G = torch.mm(features, features.t())
#
# # 归一化
# # 通过除以元素数目归一化
# return G.div(a * b * c * d)
#
#
# class StyleLoss(nn.Module):
#
# def __init__(self, target_feature):
# super(StyleLoss, self).__init__()
# self.target = gram_matrix(target_feature).detach()
#
# def forward(self, input):
# G = gram_matrix(input)
# self.loss = F.mse_loss(G, self.target)
# return input
#
#
#
# # 导入模型
# cnn = models.vgg19(pretrained=True).features.to(device).eval()
#
#
# cnn_normalization_mean = torch.tensor([0.485, 0.456, 0.406]).to(device)
# cnn_normalization_std = torch.tensor([0.229, 0.224, 0.225]).to(device)
#
# # 定义模块归一化
# class Normalization(nn.Module):
# def __init__(self, mean, std):
# super(Normalization, self).__init__()
# # .view the mean and std to make them [C x 1 x 1] so that they can
# # directly work with image Tensor of shape [B x C x H x W].
# # B is batch size. C is number of channels. H is height and W is width.
# self.mean = torch.tensor(mean).view(-1, 1, 1)
# self.std = torch.tensor(std).view(-1, 1, 1)
#
# def forward(self, img):
# # normalize img
# return (img - self.mean) / self.std
#
#
# # 期望的计算内容损失和风格损失的特征层
# content_layers_default = ['conv_4']
# style_layers_default = ['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']
#
# def get_style_model_and_losses(cnn, normalization_mean, normalization_std,
# style_img, content_img,
# content_layers=content_layers_default,
# style_layers=style_layers_default):
# # normalization module
# normalization = Normalization(normalization_mean, normalization_std).to(device)
#
# content_losses = []
# style_losses = []
#
# model = nn.Sequential(normalization)
#
# i = 0
# for layer in cnn.children():
# if isinstance(layer, nn.Conv2d):
# i += 1
# name = 'conv_{}'.format(i)
# elif isinstance(layer, nn.ReLU):
# name = 'relu_{}'.format(i)
# layer = nn.ReLU(inplace=False)
# elif isinstance(layer, nn.MaxPool2d):
# name = 'pool_{}'.format(i)
# elif isinstance(layer, nn.BatchNorm2d):
# name = 'bn_{}'.format(i)
# else:
# raise RuntimeError('Unrecognized layer: {}'.format(layer.__class__.__name__))
#
# model.add_module(name, layer)
#
# if name in content_layers:
# # 添加内容损失
# target = model(content_img).detach()
# content_loss = ContentLoss(target)
# model.add_module("content_loss_{}".format(i), content_loss)
# content_losses.append(content_loss)
#
# if name in style_layers:
# # 添加风格损失
# target_feature = model(style_img).detach()
# style_loss = StyleLoss(target_feature)
# model.add_module("style_loss_{}".format(i), style_loss)
# style_losses.append(style_loss)
#
# for i in range(len(model) - 1, -1, -1):
# if isinstance(model[i], ContentLoss) or isinstance(model[i], StyleLoss):
# break
#
# model = model[:(i + 1)]
#
# return model, style_losses, content_losses
#
#
# input_img = content_img.clone()
# # input_img = torch.randn(content_img.data.size(), device=device)
#
# plt.figure()
# imshow(input_img, title='Input Image')
#
#
#
# # 梯度下降法
# def get_input_optimizer(input_img):
# optimizer = optim.LBFGS([input_img])
# return optimizer
#
# # 定义迁移网络
# def run_style_transfer(cnn, normalization_mean, normalization_std,
# content_img, style_img, input_img, num_steps=1000,
# style_weight=1000000, content_weight=1):
# print('Building the style transfer model..')
# model, style_losses, content_losses = get_style_model_and_losses(cnn,
# normalization_mean, normalization_std, style_img, content_img)
#
# # We want to optimize the input and not the model parameters so we
# # update all the requires_grad fields accordingly
# input_img.requires_grad_(True)
# model.requires_grad_(False)
#
# optimizer = get_input_optimizer(input_img)
#
# print('Optimizing..')
# run = [0]
# while run[0] <= num_steps:
#
# def closure():
# # correct the values of updated input image
# with torch.no_grad():
# input_img.clamp_(0, 1)
#
# optimizer.zero_grad()
# model(input_img)
# style_score = 0
# content_score = 0
#
# for sl in style_losses:
# style_score += sl.loss
# for cl in content_losses:
# content_score += cl.loss
#
# style_score *= style_weight
# content_score *= content_weight
#
# loss = style_score + content_score
# loss.backward()
#
# run[0] += 1
# if run[0] % 50 == 0:
# print("run {}:".format(run))
# print('Style Loss : {:4f} Content Loss: {:4f}'.format(
# style_score.item(), content_score.item()))
# print()
#
# return style_score + content_score
#
# optimizer.step(closure)
#
# # a last correction...
# with torch.no_grad():
# input_img.clamp_(0, 1)
#
# return input_img
#
#
# output = run_style_transfer(cnn, cnn_normalization_mean, cnn_normalization_std,
# content_img, style_img, input_img)
#
# plt.figure()
# imshow(output, title=
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