Python中使用ResNet50进行图像风格迁移的编程示例

使用ResNet50进行图像风格迁移的编程示例：

首先，我们需要导入所需的库和模块：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import models, transforms

from PIL import Image
import matplotlib.pyplot as plt

然后，我们需要定义一些辅助函数来加载和预处理图像：

def load_image(image_path, transform=None, max_size=None, shape=None):
    image = Image.open(image_path)
    
    if max_size is not None:
        factor = max_size / max(image.size)
        size = tuple(int(x * factor) for x in image.size)
        image = image.resize(size, Image.ANTIALIAS)
        
    if shape is not None:
        image = image.resize(shape, Image.LANCZOS)
        
    if transform is not None:
        image = transform(image).unsqueeze(0)
        
    return image

def plot_image(image, title=None):
    image = image.squeeze(0)
    image = unloader(image)
    plt.imshow(image)
    if title is not None:
        plt.title(title)
    plt.pause(0.001)

def save_image(tensor, path):
    image = tensor.cpu().clone()
    image = image.squeeze(0)
    image = unloader(image)
    image.save(path)

接下来，我们需要加载并预处理输入图像和风格图像：

content_image = load_image("input.jpg", transform=preprocess, max_size=400)
style_image = load_image("style.jpg", transform=preprocess, shape=[content_image.size(2), content_image.size(3)])

然后，我们需要定义内容损失和风格损失函数：

class ContentLoss(nn.Module):
    def __init__(self, target):
        super(ContentLoss, self).__init__()
        self.target = target.detach()
        
    def forward(self, input):
        self.loss = nn.functional.mse_loss(input, self.target)
        return input

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 = nn.functional.mse_loss(G, self.target)
        return input

def gram_matrix(input):
    batch_size, feature_maps, height, width = input.size()
    features = input.view(batch_size * feature_maps, height * width)
    G = torch.mm(features, features.t())
    return G.div(batch_size * feature_maps * height * width)

接下来，我们需要修改ResNet50的最后一层，并定义模型的输入和输出层：

resnet = models.resnet50(pretrained=True).features

for param in resnet.parameters():
    param.requires_grad_(False)

layers = {
    '0': 'conv1',
    '5': 'layer1',
    '10': 'layer2',
    '19': 'layer3',
    '28': 'layer4'
}

content_layers = ['28']
style_layers = ['0', '5', '10', '19', '28']

model = nn.Sequential()

for name, layer in resnet._modules.items():
    model.add_module(name, layer)
    
    if name in layers:
        if name in content_layers:
            target = model(content_image).detach()
            content_loss = ContentLoss(target)
            model.add_module("content_loss", content_loss)
            
        if name in style_layers:
            target_feature = model(style_image).detach()
            style_loss = StyleLoss(target_feature)
            model.add_module("style_loss", style_loss)

接下来，我们需要定义优化器和迭代函数以及迭代次数：

optimizer = optim.LBFGS([input_image.requires_grad_()])

num_steps = 300

for i in range(num_steps):
    def closure():
        input_image.data.clamp_(0, 1)
        
        optimizer.zero_grad()
        model(input_image)
        style_score = 0
        content_score = 0
        
        for layer in model.children():
            if isinstance(layer, ContentLoss):
                content_score += layer.loss
            if isinstance(layer, StyleLoss):
                style_score += layer.loss
                
        loss = content_weight * content_score + style_weight * style_score
        loss.backward()
        
        return style_score + content_score
    
    optimizer.step(closure)

最后，我们可以保存生成的图像：

save_image(input_image, "output.jpg")

以上是使用ResNet50进行图像风格迁移的编程示例。在这个例子中，我们使用了预训练的ResNet50模型来提取图像的内容和风格特征，并通过调整输入图像来最小化内容和风格损失来实现风格迁移。